Zero tolerance policies toward adverse outcomes during labor and delivery: don't forget about the pilot.
Bibliographic record
Abstract
The publication in January 2009 of an article entitled “A Surgical Safety Checklist to Reduce Morbidity and Mortality in a Global Population,” by Haynes and colleagues in the New England Journal of Medicine,1 set off another round of recommendations for airline industry panaceas for our health care system. The Washington Post extolled the lifesaving effects of “a basic cockpit-style checklist in the operating room,” noting that “[s]urgeons ... are discovering what airline pilots learned decades ago ... .”2 Similarly, Toronto’s The Globe and Mail proclaimed “[a] simple operating room checklist, similar to the one pilots use in the cockpit before takeoff, can dramatically reduce major complications in patients and even save lives, according to a just published study.”3 Interestingly, the same day that the New England Journal of Medicine article hit the news services, US Airways Flight 1549 crashed into the Hudson River just minutes after taking off from New York’s LaGuardia Airport. All 155 people aboard were evacuated safely from the stricken aircraft. Reflecting on this miraculous outcome from a potentially disastrous situation, newspapers and the public universally singled out the pilot for his efforts. “I don’t think there’s enough praise to go around for someone who does something like this. This is something you really can’t prepare for,” said former Delta pilot Denny Walsh.4 “You really don’t practice water landings in commercial airplanes. Just the sheer expertise he demonstrated is amazing.” “It would appear that the pilot did a masterful job of landing the plane in the river and then making sure that everybody got out,” said New York City Mayor Michael R. Bloomberg.4 Although checklists and simulators will surely enhance patient safety, let us not forget the pilot. For difficult surgeries, complicated labors, or vexing medical dilemmas, someone who knows what to do is still a key component to achieving the best outcome. Effective systems are necessary, but not sufficient for patient safety. We must continue to aggressively recruit the best medical students, to train them in superb residency programs, and to maintain their skills through effective continuing medical education programs.
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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 | 0.019 | 0.102 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.014 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".