PREDICTORS OF ORAL ANTICOAGULANT-ASSOCIATED ADVERSE EVENTS IN SENIORS TRANSITIONING FROM HOSPITAL TO HOME: A RETROSPECTIVE COHORT STUDY
Bibliographic record
Abstract
Background Our objective was to identify and validate clinical and continuity of care variables associated with Oral anticoagulant (OAC)-related adverse events within 30 days of hospital discharge amongst seniors. Methods and Analysis This was a population-based retrospective cohort study of all adults aged 66 years or older who were discharged from hospital on an OAC from September 2010 to March 2015 in Ontario, Canada. The primary outcome was a composite of the time to first hospitalization or Emergency Department visit for a hemorrhage or thromboembolic event or mortality within 30 days of hospital discharge. A Cox proportional hazards model was used to determine the association between the composite outcome and a set of prespecified covariates. A split sample method was applied to validate the final model. Results We included 120 721 Ontario seniors of which 5423 suffered one of the primary adverse events. Patients discharged on a direct-acting oral anticoagulant (DOAC); dispensed the same OAC in the past 12 months; who had a history of a thromboembolic event; had a recent joint replacement or major surgery; had a cardiologist, hematologist or orthopedic surgeon as compared to a family medicine physician as the physician prescribing the OAC at discharge had a lower risk for the composite outcome. Though continuity of care was a variable in the final multivariate Cox model, it was not significant. The Cox model was stable with acceptable discrimination but poor goodness-of-fit. Conclusion In this study, we found that continuity of care as measured by outpatient follow-up in the 7 days post-discharge was not significantly associated with the composite outcome. Further exploration to improve the current model’s calibration and interpretation are required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".