“Morbidity and Mortality”: A new section in Trauma Case Reports to help us learn from our mistakes
Bibliographic record
Abstract
A new section in Trauma Case Reports to help us learn from our mistakes Morbidity and Mortality conferences were first introduced by Ernest Codman in 1904 [1].He famously suggested that surgeon competence must be evaluated and reported in a structured and repetitive manner [1].The editor of the Canadian Journal of Surgery argues that Codman's concept reached its zenith in 1983 when the Accreditation Council for Graduate Medical Education (the American equivalent of the Royal College of Physicians and Surgeons) mandated the presence of weekly M & M conferences to achieve and maintain accreditation for all surgical residency training programs [2].A recent paper has reflected upon this theme but states that continued evaluation of surgeon competence must involve both comparisons of surgeon performance to larger groups of colleagues at the individual and program levels (big data) as well as the incorporation of local expertise and sage advice in the form of collegial discussion at a formal M & M conference [3]. This new section in the Trauma Case Reports journal hopes to highlight some of this sage advice in the form of cases that have gone awry.The M & M cases presented in this section will provide food for thought to readers and then some recent "best evidence" to allow a surgeon faced with a similar tough clinical decision to make a good choice for their patients.Surgeons reading this new section in the Trauma Case Reports journal will: 1) see similar patients that they have in their practice, 2) identify situations where a good choice may save a patient from a bad result, 3) note the best recent clinical evidence available, 4) apply a consistent, prospective process for enhanced decision making when presented with a difficult patient dilemma.We, the editors, hope that this new section will meet your expectations and make it easier for you to come to good patient care decisions.There is no doubt that the clinical problems presented in this section, will allow your future patients in your practice to have a better outcome.
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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 | 0.002 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.002 | 0.002 |
| 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".