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Record W2394384542 · doi:10.1213/ane.0000000000001383

A Comparison of Web-Based with Traditional Classroom-Based Training of Lung Ultrasound for the Exclusion of Pneumothorax

2016· article· en· W2394384542 on OpenAlexfundno aff
Thomas Edrich, Matthias Stopfkuchen-Evans, Patrick Scheiermann, Markus Heim, Wilma Chan, Michael B. Stone, Daniel Dankl, Jonathan Aichner, Dominik Hinzmann, Pingping Song, Ashley L. Szabo, György Frendl, Kamen Vlassakov, Dirk Varelmann

Bibliographic record

VenueAnesthesia & Analgesia · 2016
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsnot available
FundersConcordia University of Edmonton
KeywordsMedicinePneumothoraxFocused assessment with sonography for traumaRandomized controlled trialLungThorax (insect anatomy)Medical physicsSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Lung ultrasound (LUS) is a well-established method that can exclude pneumothorax by demonstration of pleural sliding and the associated ultrasound artifacts. The positive diagnosis of pneumothorax is more difficult to obtain and relies on detection of the edge of a pneumothorax, called the "lung point." Yet, anesthesiologists are not widely taught these techniques, even though their patients are susceptible to pneumothorax either through trauma or as a result of central line placement or regional anesthesia techniques performed near the thorax. In anticipation of an increased training demand for LUS, efficient and scalable teaching methods should be developed. In this study, we compared the improvement in LUS skills after either Web-based or classroom-based training. We hypothesized that Web-based training would not be inferior to "traditional" classroom-based training beyond a noninferiority limit of 10% and that both would be superior to no training. Furthermore, we hypothesized that this short training session would lead to LUS skills that are similar to those of ultrasound-trained emergency medicine (EM) physicians. METHODS: After a pretest, anesthesiologists from 4 academic teaching hospitals were randomized to Web-based (group Web), classroom-based (group class), or no training (group control) and then completed a posttest. Groups Web and class returned for a retention test 4 weeks later. All 3 tests were similar, testing both practical and theoretical knowledge. EM physicians (group EM) performed the pretest only. Teaching for group class consisted of a standardized PowerPoint lecture conforming to the Consensus Conference on LUS followed by hands-on training. Group Web received a narrated video of the same PowerPoint presentation, followed by an online demonstration of LUS that also instructs the viewer to perform an LUS on himself using a clinically available ultrasound machine and submit smartphone snapshots of the resulting images as part of a portfolio system. Group Web received no other hands-on training. RESULTS: Groups Web, class, control, and EM contained 59, 59, 20, and 42 subjects. After training, overall test results of groups Web and class improved by a mean of 42.9% (±18.1% SD) and 39.2% (±19.2% SD), whereas the score of group control did not improve significantly. The test improvement of group Web was not inferior to group class. The posttest scores of groups Web and class were not significantly different from group EM. In comparison with the posttests, the retention test scores did not change significantly in either group. CONCLUSIONS: When training anesthesiologists to perform LUS for the exclusion of pneumothorax, we found that Web-based training was not inferior to traditional classroom-based training and was effective, leading to test scores that were similar to a group of clinicians experienced in LUS.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.272
Threshold uncertainty score0.405

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.076
GPT teacher head0.347
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations60
Published2016
Admission routes1
Has abstractyes

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