Which older emergency patients are at risk of intracranial bleeding after a fall? A protocol to derive a clinical decision rule for the emergency department
Bibliographic record
Abstract
INTRODUCTION: Falling on level ground is now the most common cause of traumatic intracranial bleeding worldwide. Older adults frequently present to the emergency department (ED) after falling. It can be challenging for clinicians to determine who requires brain imaging to rule out traumatic intracranial bleeding, and often head injury decision rules do not apply to older adults who fall. The goal of our study is to derive a clinical decision rule, which will identify older adults who present to the ED after a fall who do not have clinically important intracranial bleeding. METHODS AND ANALYSIS: This is a prospective cohort study enrolling patients aged 65 years or older, who present to the ED of 11 hospitals in Canada and the USA within 48 hours of having a fall. Patients are included if they fall on level ground, off a chair, toilet seat or out of bed. The primary outcome is the diagnosis of clinically important intracranial bleeding within 42 days of the index ED visit. An independent adjudication committee will determine the primary outcome, blinded to all other data. We are collecting data on 17 potential predictor variables. The treating physician completes a study data form at the time of initial assessment, prior to brain imaging. Data extraction is supplemented by an independent, structured electronic medical record review. We will perform binary recursive partitioning using Classification and Regression Trees to derive a clinical decision rule. ETHICS AND DISSEMINATION: The study was initially approved by the Hamilton Integrated Research Ethics Committee and subsequently approved by the research ethics boards governing all participating sites. We will disseminate our results by journal publication, presentation at international meetings and social media. TRIAL REGISTRATION NUMBER: 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 | 0.068 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".