Derivation of the Falls Decision Rule to exclude intracranial bleeding without head CT in older adults who have fallen
Bibliographic record
Abstract
BACKGROUND: Ground-level falls are common among older adults and are the most frequent cause of traumatic intracranial bleeding. The aim of this study was to derive a clinical decision rule that safely excludes clinically important intracranial bleeding in older adults who present to the emergency department after a fall, without the need for a computed tomography (CT) scan of the head. METHODS: This prospective cohort study in 11 emergency departments in Canada and the United States enrolled patients aged 65 years or older who presented after falling from standing on level ground, off a chair or toilet seat, or out of bed. We collected data on 17 potential predictor variables. The primary outcome was the diagnosis of clinically important intracranial bleeding within 42 days of the index emergency department visit. An independent adjudication committee, blinded to baseline data, determined the primary outcome. We derived a clinical decision rule using logistic regression. RESULTS: The cohort included 4308 participants, with a median age of 83 years; 2770 (64%) were female, 1119 (26%) took anticoagulant medication and 1567 (36%) took antiplatelet medication. Of the participants, 139 (3.2%) received a diagnosis of clinically important intracranial bleeding. We developed a decision rule indicating that no head CT is required if there is no history of head injury on falling; no amnesia of the fall; no new abnormality on neurologic examination; and the Clinical Frailty Scale score is less than 5. Rule sensitivity was 98.6% (95% confidence interval [CI] 94.9%-99.6%), specificity was 20.3% (95% CI 19.1%-21.5%) and negative predictive value was 99.8% (95% CI 99.2%-99.9%). INTERPRETATION: ClinicalTrials. gov, no. NCT03745755.
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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 | 0.015 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".