Emergency physician gender and head computed tomography orders for older adults who have fallen
Bibliographic record
Abstract
OBJECTIVE: Physicians vary in their computed tomography (CT) scan usage. It remains unclear how physician gender relates to clinical practice or patient outcomes. The aim of this study was to assess the association between physician gender and decision to order head CT scans for older emergency patients who had fallen. METHODS: This was a secondary analysis of a prospective observational cohort study conducted in 11 hospital emergency departments (EDs) in Canada and the United States. The primary study enrolled patients who were 65 years and older who presented to the ED after a fall. The analysis evaluated treating physician gender adjusted for multiple clinical variables. Primary analysis used a hierarchical logistic regression model to evaluate the association between treating physician gender and the patient receiving a head CT scan. Secondary analysis reported the adjusted odds ratio (OR) for diagnosing intracranial bleeding by physician gender. RESULTS: There were 3663 patients and 256 physicians included in the primary analysis. In the adjusted analysis, women physicians were no more likely to order a head CT than men (OR 1.26, 95% confidence interval 0.98-1.61). In the secondary analysis of 2294 patients who received a head CT, physician gender was not associated with finding a clinically important intracranial bleed. CONCLUSIONS: There was no significant association between physician gender and ordering head CT scans for older emergency patients who had fallen. For patients where CT scans were ordered, there was no significant relationship between physician gender and the diagnosis of clinically important intracranial bleeding.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".