Physicians' Influence over Decisions to Forego Life Support
Bibliographic record
Abstract
OBJECTIVE: To quantify the association between individual physicians and decisions to limit use of life supporting therapies for critically ill patients. STUDY DESIGN: Prospective, observational data collected 2002-2005 in the adult medical intensive care unit (ICU) of a publicly owned teaching hospital. Nine intensivists staff this closed-model ICU; in rotating 2 week blocks of time one intensivist is responsible for directing all care. In order to uniquely associate care with individual physicians, eligible patients were cared for by a single intensivist throughout their ICU stay. Life support decisions were identified as orders to withhold or withdraw any form of life supporting medical therapy, including cardiopulmonary resuscitation (CPR), defibrillation, invasive mechanical ventilation, vasoactive drugs, and renal replacement therapy. We used multivariable Cox modeling to identify variables associated with decisions to limit use of life support. The association with the individual physicians was assessed as the hazard ratios of indicator variables representing the individual physicians. RESULTS: A decision to limit use of life support was made in 191 (14.0%) of 1363 ICU admissions. The hazard ratios associated with individual intensivists spanned a 15-fold range (0.069-1.042; p = 0.0003). Decisions to limit life support were more strongly related to the identity of the intensivist than to comorbid conditions, acute diagnostic category, and the source of ICU admission. CONCLUSIONS: We have, for the first time, quantified the association between individual physicians and decisions made to limit life support for critically ill patients. More research is needed to understand the nature and implications of this association.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".