Comparative Analysis of Taper Point and Reverse Cutting Needles on Skin Puncture Force.

Maloney ME ，Potter CT ，Chun BD ，Montilla RD ，Schanbacher CF ... -  《-》

Impact of Needle Design and Suture Gauge on Tissue Tearing During Skin Suturing: A Comparative Analysis.

Surgeons face numerous choices in selecting sutures for skin closure, with potential adverse effects such as tissue tearing. To investigate the influence of needle design and suture gauge on tissue tearing during suturing procedures. The authors tested the tear-through force in Newtons for 3 needle types and 3 suture gauges using an artificial skin model and a professional-grade tensiometer. Suture material was secured into the skin model, and force was applied to the suture at a constant rate, resulting in tearing. Force-displacement and force-time curves were generated. Evaluation included conventional cutting (PC-3), reverse cutting (PS-3), and taper point (BB) needles with a 5-0 polypropylene suture. In addition, nylon sutures with a reverse cutting needle (PS-2) were tested at 3 suture gauges (5-0, 4-0, 3-0). The mean tear-through forces for PC-3, PS-3, and BB were 3.26 N, 3.75 N, and 4.07 N, respectively. For the 5-0, 4-0, and 3-0 nylon sutures, the mean tear-through forces were 3.44 N, 3.81 N, and 4.04 N, respectively. Statistical analysis revealed a significant impact of suture gauge size ( p < .001) and needle geometry ( p < .001) on tear-through force. Larger suture diameter and taper needles minimize tissue tearing.

Potter CT ，Maloney ME ，Riopelle AM ，Fudem GM ，Schanbacher CF ... -  《-》

Conventional ultrasonography enabled with augmented reality needle guidance for percutaneous kidney access: An innovative methodologies randomized controlled trial.

Successful needle puncture of the renal collecting system is a critical but difficult procedure in percutaneous nephrolithotomy (PCNL). Although fluoroscopy and ultrasound are the standard imaging techniques to guide puncture during PCNL, both have known limitations. To assess the feasibility and safety of a new navigation system for needle puncture in ultrasound-guided PCNL. This study employed a single-center randomized controlled trial (RCT) design to assess the feasibility and safety of a new navigation system for needle puncture in ultrasound-guided PCNL. Conducted between May 2021 and November 2021, the trial utilized computer-generated random numbers for participant allocation to control for selection bias. The trial was executed at the *********, which serves as an academic medical center. All patients who met the inclusion criteria were randomly divided into two groups, with 29 patients in each group. One group underwent PCNL procedures using the new navigation system, while the control group underwent standard ultrasound-guided PCNL procedures. Included patients had renal pelvis or caliceal calculi larger than 2.0 cm in diameter or had multiple or staghorn stones. The puncture procedure was performed with the support of real-time ultrasound imaging and visual guidance displayed on the screen. The primary outcome was system feasibility and puncture success rate. Secondary outcomes included puncture time, total surgical time, number of attempts, post-procedure complications, and one-year and three-year stone recurrence rates. Stone clearance was defined by postoperative CT. Descriptive statistics summarized patient demographics, stone size, and location. Independent samples t-tests analyzed puncture time and total surgical time. Chi-square or Fisher's exact tests compared stone clearance, complications, socioeconomic status, renal hydronephrosis, stone location, race, and medical history. Linear regression examined the correlation between BMI and puncture time. Significance was set at P<0.05. For all 58 patients undergoing PCNL, needle punctures of the renal collecting system were completed with a success rate of 100%. The average time from planning the puncture protocol to successful puncture was significantly shorter in the AcuSee guidance system group (3.12 min, range 0.2-6.88 min) compared to the standard ultrasound-guided group (7.58 min, range 5.41-10.68 min), representing a reduction of approximately 59%. The total surgical time was also shorter in the AcuSee group for patients with no and mild hydronephrosis (P<0.05). Complication rates were lower in the AcuSee group, with no major complications observed. However, 3 patients in the standard ultrasound-guided group have adverse effects after the PCNL procedure. The one-year stone recurrence rate was significantly lower in the AcuSee group (3.4%) compared to the standard group (24.1%), and the three-year recurrence rate was also lower (6.9% vs. 41.4%). Patient-specific factors such as BMI, renal morphology, and prior surgical history did not significantly affect the performance of the AcuSee system. We report the first clinical application of a new navigation system for needle puncture in ultrasound-guided PCNL. It has been demonstrated that it is feasible and safe compared to the standard ultrasound-guided group in percutaneous renal puncture. This technology provides intuitive and easy-to-use visual guidance, which may facilitate safe, accurate and fast needle puncture of the kidney.

Xu C ，Li A ，Peng Y ，Li L ，Xiong G ，Fan Y ，Zhao Z ，Li X ，Zhang X ，Zheng Y ，Zhang C ，Lv C ，Li X ，Wang G ，Xia Y ，Wang P ，Yao L ... -  《-》

How Does ChatGPT Use Source Information Compared With Google? A Text Network Analysis of Online Health Information.

The lay public is increasingly using ChatGPT (a large language model) as a source of medical information. Traditional search engines such as Google provide several distinct responses to each search query and indicate the source for each response, but ChatGPT provides responses in paragraph form in prose without providing the sources used, which makes it difficult or impossible to ascertain whether those sources are reliable. One practical method to infer the sources used by ChatGPT is text network analysis. By understanding how ChatGPT uses source information in relation to traditional search engines, physicians and physician organizations can better counsel patients on the use of this new tool. (1) In terms of key content words, how similar are ChatGPT and Google Search responses for queries related to topics in orthopaedic surgery? (2) Does the source distribution (academic, governmental, commercial, or form of a scientific manuscript) differ for Google Search responses based on the topic's level of medical consensus, and how is this reflected in the text similarity between ChatGPT and Google Search responses? (3) Do these results vary between different versions of ChatGPT? We evaluated three search queries relating to orthopaedic conditions: "What is the cause of carpal tunnel syndrome?," "What is the cause of tennis elbow?," and "Platelet-rich plasma for thumb arthritis?" These were selected because of their relatively high, medium, and low consensus in the medical evidence, respectively. Each question was posed to ChatGPT version 3.5 and version 4.0 20 times for a total of 120 responses. Text network analysis using term frequency-inverse document frequency (TF-IDF) was used to compare text similarity between responses from ChatGPT and Google Search. In the field of information retrieval, TF-IDF is a weighted statistical measure of the importance of a key word to a document in a collection of documents. Higher TF-IDF scores indicate greater similarity between two sources. TF-IDF scores are most often used to compare and rank the text similarity of documents. Using this type of text network analysis, text similarity between ChatGPT and Google Search can be determined by calculating and summing the TF-IDF for all keywords in a ChatGPT response and comparing it with each Google search result to assess their text similarity to each other. In this way, text similarity can be used to infer relative content similarity. To answer our first question, we characterized the text similarity between ChatGPT and Google Search responses by finding the TF-IDF scores of the ChatGPT response and each of the 20 Google Search results for each question. Using these scores, we could compare the similarity of each ChatGPT response to the Google Search results. To provide a reference point for interpreting TF-IDF values, we generated randomized text samples with the same term distribution as the Google Search results. By comparing ChatGPT TF-IDF to the random text sample, we could assess whether TF-IDF values were statistically significant from TF-IDF values obtained by random chance, and it allowed us to test whether text similarity was an appropriate quantitative statistical measure of relative content similarity. To answer our second question, we classified the Google Search results to better understand sourcing. Google Search provides 20 or more distinct sources of information, but ChatGPT gives only a single prose paragraph in response to each query. So, to answer this question, we used TF-IDF to ascertain whether the ChatGPT response was principally driven by one of four source categories: academic, government, commercial, or material that took the form of a scientific manuscript but was not peer-reviewed or indexed on a government site (such as PubMed). We then compared the TF-IDF similarity between ChatGPT responses and the source category. To answer our third research question, we repeated both analyses and compared the results when using ChatGPT 3.5 versus ChatGPT 4.0. The ChatGPT response was dominated by the top Google Search result. For example, for carpal tunnel syndrome, the top result was an academic website with a mean TF-IDF of 7.2. A similar result was observed for the other search topics. To provide a reference point for interpreting TF-IDF values, a randomly generated sample of text compared with Google Search would have a mean TF-IDF of 2.7 ± 1.9, controlling for text length and keyword distribution. The observed TF-IDF distribution was higher for ChatGPT responses than for random text samples, supporting the claim that keyword text similarity is a measure of relative content similarity. When comparing source distribution, the ChatGPT response was most similar to the most common source category from Google Search. For the subject where there was strong consensus (carpal tunnel syndrome), the ChatGPT response was most similar to high-quality academic sources rather than lower-quality commercial sources (TF-IDF 8.6 versus 2.2). For topics with low consensus, the ChatGPT response paralleled lower-quality commercial websites compared with higher-quality academic websites (TF-IDF 14.6 versus 0.2). ChatGPT 4.0 had higher text similarity to Google Search results than ChatGPT 3.5 (mean increase in TF-IDF similarity of 0.80 to 0.91; p < 0.001). The ChatGPT 4.0 response was still dominated by the top Google Search result and reflected the most common search category for all search topics. ChatGPT responses are similar to individual Google Search results for queries related to orthopaedic surgery, but the distribution of source information can vary substantially based on the relative level of consensus on a topic. For example, for carpal tunnel syndrome, where there is widely accepted medical consensus, ChatGPT responses had higher similarity to academic sources and therefore used those sources more. When fewer academic or government sources are available, especially in our search related to platelet-rich plasma, ChatGPT appears to have relied more heavily on a small number of nonacademic sources. These findings persisted even as ChatGPT was updated from version 3.5 to version 4.0. Physicians should be aware that ChatGPT and Google likely use the same sources for a specific question. The main difference is that ChatGPT can draw upon multiple sources to create one aggregate response, while Google maintains its distinctness by providing multiple results. For topics with a low consensus and therefore a low number of quality sources, there is a much higher chance that ChatGPT will use less-reliable sources, in which case physicians should take the time to educate patients on the topic or provide resources that give more reliable information. Physician organizations should make it clear when the evidence is limited so that ChatGPT can reflect the lack of quality information or evidence.

Shen OY ，Pratap JS ，Li X ，Chen NC ，Bhashyam AR ... -  《-》

The effect of sample site and collection procedure on identification of SARS-CoV-2 infection.

Sample collection is a key driver of accuracy in the diagnosis of SARS-CoV-2 infection. Viral load may vary at different anatomical sampling sites and accuracy may be compromised by difficulties obtaining specimens and the expertise of the person taking the sample. It is important to optimise sampling accuracy within cost, safety and accessibility constraints. To compare the sensitivity of different sampling collection sites and methods for the detection of current SARS-CoV-2 infection with any molecular or antigen-based test. Electronic searches of the Cochrane COVID-19 Study Register and the COVID-19 Living Evidence Database from the University of Bern (which includes daily updates from PubMed and Embase and preprints from medRxiv and bioRxiv) were undertaken on 22 February 2022. We included independent evaluations from national reference laboratories, FIND and the Diagnostics Global Health website. We did not apply language restrictions. We included studies of symptomatic or asymptomatic people with suspected SARS-CoV-2 infection undergoing testing. We included studies of any design that compared results from different sample types (anatomical location, operator, collection device) collected from the same participant within a 24-hour period. Within a sample pair, we defined a reference sample and an index sample collected from the same participant within the same clinical encounter (within 24 hours). Where the sample comparison was different anatomical sites, the reference standard was defined as a nasopharyngeal or combined naso/oropharyngeal sample collected into the same sample container and the index sample as the alternative anatomical site. Where the sample comparison was concerned with differences in the sample collection method from the same site, we defined the reference sample as that closest to standard practice for that sample type. Where the sample pair comparison was concerned with differences in personnel collecting the sample, the more skilled or experienced operator was considered the reference sample. Two review authors independently assessed the risk of bias and applicability concerns using the QUADAS-2 and QUADAS-C checklists, tailored to this review. We present estimates of the difference in the sensitivity (reference sample (%) minus index sample sensitivity (%)) in a pair and as an average across studies for each index sampling method using forest plots and tables. We examined heterogeneity between studies according to population (age, symptom status) and index sample (time post-symptom onset, operator expertise, use of transport medium) characteristics. This review includes 106 studies reporting 154 evaluations and 60,523 sample pair comparisons, of which 11,045 had SARS-CoV-2 infection. Ninety evaluations were of saliva samples, 37 nasal, seven oropharyngeal, six gargle, six oral and four combined nasal/oropharyngeal samples. Four evaluations were of the effect of operator expertise on the accuracy of three different sample types. The majority of included evaluations (146) used molecular tests, of which 140 used RT-PCR (reverse transcription polymerase chain reaction). Eight evaluations were of nasal samples used with Ag-RDTs (rapid antigen tests). The majority of studies were conducted in Europe (35/106, 33%) or the USA (27%) and conducted in dedicated COVID-19 testing clinics or in ambulatory hospital settings (53%). Targeted screening or contact tracing accounted for only 4% of evaluations. Where reported, the majority of evaluations were of adults (91/154, 59%), 28 (18%) were in mixed populations with only seven (4%) in children. The median prevalence of confirmed SARS-CoV-2 was 23% (interquartile (IQR) 13%-40%). Risk of bias and applicability assessment were hampered by poor reporting in 77% and 65% of included studies, respectively. Risk of bias was low across all domains in only 3% of evaluations due to inappropriate inclusion or exclusion criteria, unclear recruitment, lack of blinding, nonrandomised sampling order or differences in testing kit within a sample pair. Sixty-eight percent of evaluation cohorts were judged as being at high or unclear applicability concern either due to inflation of the prevalence of SARS-CoV-2 infection in study populations by selectively including individuals with confirmed PCR-positive samples or because there was insufficient detail to allow replication of sample collection. When used with RT-PCR • There was no evidence of a difference in sensitivity between gargle and nasopharyngeal samples (on average -1 percentage points, 95% CI -5 to +2, based on 6 evaluations, 2138 sample pairs, of which 389 had SARS-CoV-2). • There was no evidence of a difference in sensitivity between saliva collection from the deep throat and nasopharyngeal samples (on average +10 percentage points, 95% CI -1 to +21, based on 2192 sample pairs, of which 730 had SARS-CoV-2). • There was evidence that saliva collection using spitting, drooling or salivating was on average -12 percentage points less sensitive (95% CI -16 to -8, based on 27,253 sample pairs, of which 4636 had SARS-CoV-2) compared to nasopharyngeal samples. We did not find any evidence of a difference in the sensitivity of saliva collected using spitting, drooling or salivating (sensitivity difference: range from -13 percentage points (spit) to -21 percentage points (salivate)). • Nasal samples (anterior and mid-turbinate collection combined) were, on average, 12 percentage points less sensitive compared to nasopharyngeal samples (95% CI -17 to -7), based on 9291 sample pairs, of which 1485 had SARS-CoV-2. We did not find any evidence of a difference in sensitivity between nasal samples collected from the mid-turbinates (3942 sample pairs) or from the anterior nares (8272 sample pairs). • There was evidence that oropharyngeal samples were, on average, 17 percentage points less sensitive than nasopharyngeal samples (95% CI -29 to -5), based on seven evaluations, 2522 sample pairs, of which 511 had SARS-CoV-2. A much smaller volume of evidence was available for combined nasal/oropharyngeal samples and oral samples. Age, symptom status and use of transport media do not appear to affect the sensitivity of saliva samples and nasal samples. When used with Ag-RDTs • There was no evidence of a difference in sensitivity between nasal samples compared to nasopharyngeal samples (sensitivity, on average, 0 percentage points -0.2 to +0.2, based on 3688 sample pairs, of which 535 had SARS-CoV-2). When used with RT-PCR, there is no evidence for a difference in sensitivity of self-collected gargle or deep-throat saliva samples compared to nasopharyngeal samples collected by healthcare workers when used with RT-PCR. Use of these alternative, self-collected sample types has the potential to reduce cost and discomfort and improve the safety of sampling by reducing risk of transmission from aerosol spread which occurs as a result of coughing and gagging during the nasopharyngeal or oropharyngeal sample collection procedure. This may, in turn, improve access to and uptake of testing. Other types of saliva, nasal, oral and oropharyngeal samples are, on average, less sensitive compared to healthcare worker-collected nasopharyngeal samples, and it is unlikely that sensitivities of this magnitude would be acceptable for confirmation of SARS-CoV-2 infection with RT-PCR. When used with Ag-RDTs, there is no evidence of a difference in sensitivity between nasal samples and healthcare worker-collected nasopharyngeal samples for detecting SARS-CoV-2. The implications of this for self-testing are unclear as evaluations did not report whether nasal samples were self-collected or collected by healthcare workers. Further research is needed in asymptomatic individuals, children and in Ag-RDTs, and to investigate the effect of operator expertise on accuracy. Quality assessment of the evidence base underpinning these conclusions was restricted by poor reporting. There is a need for further high-quality studies, adhering to reporting standards for test accuracy studies.

Davenport C ，Arevalo-Rodriguez I ，Mateos-Haro M ，Berhane S ，Dinnes J ，Spijker R ，Buitrago-Garcia D ，Ciapponi A ，Takwoingi Y ，Deeks JJ ，Emperador D ，Leeflang MMG ，Van den Bruel A ，Cochrane COVID-19 Diagnostic Test Accuracy Group ... -  《Cochrane Database of Systematic Reviews》