Abstract 16821: Impact of Patient Transfers on Outcome Evaluations Following Acute Myocardial Infarction
Bibliographic record
Abstract
Introduction: Patients hospitalized for an AMI are commonly transferred to tertiary care facilities to receive advanced procedures. In the United States, transferred patients are often excluded from outcome evaluations due to limitations in ascertaining outcomes. In Canada, centralized administrative and clinical databases allow for linkage of outcomes across institutions. This study evaluated the influence of excluding transferred AMI patients on estimates of age and sex-specific 30-day mortality rates over a 10-year period. Methods: All incident AMI hospitalizations (ICD-9: 410 and ICD-10: I21, I22) to acute care hospitals in British Columbia from 2000 to 2009 were identified through the Discharge Abstract Database and linked to mortality through the Vital Statistics registry. A transfer was identified as a discharge from the index AMI admission hospital to another hospital. Thirty-day mortality was defined as any death within 30 days of the index hospitalization. A logistic regression model was used to compare transfer rate by age (20-55, 56-64, 65-74 and ≥ 75 years), sex and admission year. Thirty-day mortality rates by sex and age were calculated with and without transferred patients. Results: Of the 67,444 AMI hospitalizations identified, 38.6% involved at least one transfer. Overall, younger patients were more likely to be transferred. Transfer rates increased over time in all age groups but with different slopes (p = 0.02) regardless of sex, and differed significantly between sexes only in those ≥ 75 (p < 0.001; Fig. A). Thirty-day mortality rates were overestimated in each age-sex category when transferred patients were excluded, compared to rates that included all patients, irrespective of transfer status (Fig. B). Conclusions: The rate of patient transfers post AMI has increased over time. Excluding transferred patients results in overestimates of 30-day mortality, and thus impacts outcome assessments. This impact is expected to increase over time.
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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 | 0.012 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".