Reliability of patient‐reported outcome measures: Hemorrhage, anticoagulant, antiplatelet medication use
Bibliographic record
Abstract
BACKGROUND: Most antithrombotic medication users are older adults. Patient-reported outcome measures are commonly used in clinical research on antithrombotic medication, such as the diagnosis of intracranial hemorrhage. OBJECTIVES: To determine the reliability of patient-reported intracranial hemorrhage, anticoagulant and platelet aggregation inhibitor use in the older adult population. PATIENTS/METHODS: We conducted a secondary analysis of a prospective, observational cohort study of older adults who presented to the emergency department with a fall. The primary outcome was diagnosis of intracranial bleeding. We compared patient-reported intracranial bleeding to structured chart review with adjudication. We also compared patient-reported use of antiplatelet and anticoagulant medication to physician-reported medication use supplemented with structured chart review. We calculated the diagnostic accuracy of the patient-reported outcomes using our comparators as the reference standard. RESULTS: Exact agreement for patient-reported intracranial bleeds was 95%, with a Cohen's kappa of 0.30 (95% confidence interval [CI], 0.15-0.45). The sensitivity was 36.7% (95% CI, 20.6%-56.1%) and specificity 97.2% (95% CI, 95.8%-98.1%). For anticoagulant medication use, exact agreement was 87%, Cohen's kappa 0.66 (95% CI, 0.63-0.72), sensitivity 84.0% (95% CI, 79.3%-83.8%), and specificity 87.6% (95% CI, 85.1%-89.7%). For antiplatelet medication use, exact agreement was 77%, Cohen's kappa 0.50 (95% CI, 0.44-0.55), sensitivity 68.7% (95% CI, 64.0%-73.1%), and specificity 81.2% (95% CI, 78.0-83.8%). CONCLUSIONS: Patient-reported outcome and exposure data were unreliable in this study. Our findings have a bearing on future research study design.
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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 | 0.038 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".