Factors associated with physicians’ predictions of six-month mortality in critically ill patients
Bibliographic record
Abstract
BACKGROUND: Physician's estimates of a patient's prognosis are an important component in shared decision-making. However, the variables influencing physician's judgments are not well understood. We aimed to determine which physician and patient factors are associated with physicians' predictions of critically ill patients' six-month mortality and the accuracy and confidence of these predictions. METHODS: Prospective cohort study evaluating physicians' predictions of six-month mortality. Using univariate and multivariable generalized estimating equations, we assessed the association between baseline physician and patient characteristics with predictions of six-month death, as well as accuracy and confidence of these predictions. RESULTS: Our cohort was comprised 300 patients and 47 physicians. Physicians were asked to predict if patients would be alive or dead at six months and to report their confidence in these predictions. Physicians predicted that 99 (33%) patients would die. The key factors associated with both the direction and accuracy of prediction were older age of the patient, the presence of malignancy, being in a medical ICU, and higher APACHE III scores. The factors associated with lower confidence included older physician age, being in a medical ICU and higher APACHE III score. CONCLUSIONS: Patient level factors are associated with predictions of mortality at six months. The accuracy and confidence of the predictions are associated with both physician and patients' factors. The influence of these factors should be considered when physicians reflect on how they make predictions for critically ill patients.
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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 | 0.002 | 0.029 |
| 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".