Management of Simulated Oxygen Supply Failure and Expiratory Valve Malfunction: Is There A Curriculum Gap?
Bibliographic record
Abstract
BACKGROUND: In a recent publication, deficits in the management of a simulated oxygen pipeline failure in a Canadian residency program were demonstrated using high fidelity simulation based training (1). Only half of 12 fourth year residents recognized oxygen pipeline failure and opened the oxygen cylinder on the machine. The alarming findings, which may compromise patient safety, led us to look for possible gaps among Israeli residents. MATERIAL AND METHODS: Two simulation based scenarios were developed and used during the Israeli Board Examination in Anesthesiology. In the first scenario performed by 10 examinees, oxygen pipeline failure, and in the second scenario performed by other 9 examinees expiratory valve malfunction occurred during simulated pediatric anesthesia scenarios. Performance was scored in real time by two anesthesiologists using a performance checklist independently. RESULTS: In the first scenario - 9 out of 10 examinees recognized the O2 supply and pressure alarms, successfully opened the O2 cylinder on the machine and disconnected the anesthesia machine from the central oxygen supply. However, only 6 of the examinees could fully explain how they can minimize the use of oxygen from the cylinder (using hand bag ventilation and not mechanical ventilation, using low flows, and adding air or nitrous oxide to oxygen). In the second scenario - 9 out of 9 participants recognized the abnormal capnographic signal and ventilated the patient using a self inflating bag, 8 out of 9 actually found the technical problem, however only 6 out of 9 offered the full differential diagnosis for the situation (exhausted absorbent, inadvertent administration of carbon dioxide, excessive dead space, leak in inspiratory limb of circle, capnograph artifact). CONCLUSION: Our results suggest that the management of oxygen supply failure and expiratory valve malfunction was satisfactory among experienced residents attending the Board Examination in Israel. However, deficiencies in understanding were manifested by the less then optimal differential diagnosis offered. Although our results are different from the Canadian ones, the process of using alarming information from one medical system to assess another medical system represents the value of sharing information and the value of simulation based performance assessment to improve patient safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".