Lung Sliding Identification Is Less Accurate in the Left Hemithorax
Bibliographic record
Abstract
OBJECTIVES: The aim of our study was to compare the accuracy of lung sliding identification for the left and right hemithoraxes, using prerecorded short US sequences, in a group of physicians with mixed clinical and US training. METHODS: A total of 140 US sequences of a complete respiratory cycle were recorded in the operating room. Each sequence was divided in two, yielding 140 sequences of present lung sliding and 140 sequences of absent lung sliding. Of these 280 sequences, 40 were randomly repeated to assess intraobserver variability, for a total of 320 sequences. Descriptive data, the mean accuracy of each participant, as well as the rate of correct answers for each of the original 280 sequences were tabulated and compared for different subgroups of clinical and US training. A video with examples of present and absent lung sliding and a lung pulse was shown before testing. RESULTS: Two sessions were planned to facilitate the participation of 75 clinicians. In the first group, the rate of accurate lung sliding identification was lower in the left hemithorax than in the right (67.0% [interquartile range (IQR), 43.0-83.0] versus 80.0% [IQR, 57.0-95.0]; P < .001). In the second group, the rate of accurate lung sliding identification was also lower in the left hemithorax than in the right (76.3% [IQR, 42.9-90.9] versus 88.7% [IQR, 63.1-96.9]; P = .001). Mean accuracy rates were 67.5% (95% confidence interval, 65.7-69.4) in the first group and 73.1% (95% confidence interval, 70.7-75.5) in the second (P < .001). CONCLUSIONS: Lung sliding identification seems less accurate in the left hemithorax when using a short US examination. This study was done on recorded US sequences and should be repeated in a live clinical situation to confirm our results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".