Bibliographic record
Abstract
To the Editor Hall et al. claim that paramedic endotracheal intubation (ETI) training using human simulators is equivalent to training using live human operating room (OR) patients.1 We are not convinced and believe the question and results are framed in error.The ultimate goal of paramedic airway training is to ensure the best performance of ETI in the uncontrolled out-of-hospital setting.The study by Hall et al. demonstrates only that compared with OR-trained individuals, human simulator-trained paramedic students can intubate equivalently in the controlled and closely supervised operating room setting.The study does not indicate how those students will perform in the chaos of the field setting.Furthermore, multiple studies reiterate that the manner of out-of-hospital ETI may be as important, if not more important, than ETI success.[1][2][3] The study by Hall et al. identifies selected secondary events (esophageal placement, dental trauma, airway bleeding, and oxygen desaturation) in a controlled setting, but the value of these measures as surrogates for field performance is dubious.Two prior efforts to associate mannequin-based training with field ETI performance had significant limitations and provided few useful answers.4,5 Live OR training is a traditional but proven method for exposing students to the subtleties of ETI on a wide range of patients.Due to clinical, logistical, and medicolegal barriers, educators nationally face difficulty obtaining adequate OR time for training paramedic students.The solution to this crisis is to engage our anesthesia colleagues in the paramedic educational process, not to resort to undertested and uncertain training modalities.Human simulation may potentially play an adjunct role in teaching and learning ETI.However, the results of this efficacy trial (erroneously termed an ''effectiveness'' trial) should not be used to conclude that simulators can replace live ETI experience.
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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 | 0.006 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.053 | 0.047 |
| Insufficient payload (model declined to judge) | 0.028 | 0.017 |
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".