An exploration of the determinants of mortality among hospitalized heart failure patients in Ontario, Canada
Bibliographic record
Abstract
In this thesis, we performed studies that serve as a foundation for research in heart failure mortality outcome assessment. Validation studies of the coding of heart failure in the Canadian Institute for Health Information discharge abstract database were performed. Comparison with clinical data sources confirmed the high predictive value of coding of heart failure in the administrative dataset, but undercoding of comorbidities. Using administrative datasets, trends in heart failure outcomes and the association with drug therapies were examined. Although significant changes in drug therapy were observed over time, crude mortality rates after index heart failure admission continued to be high, decreasing by 1.3% from 1992/93 to 1999/00. There was no decrease in 30-day mortality rates during this time. Since mortality remains high, a prognostic model for mortality prediction was developed and validated using clinical databases. Features predictive of mortality included age, presentation vital signs, routine laboratory tests, and comorbid conditions. Heart failure is associated with high rates of mortality and re-hospitalization. Variations in cardiovascular disease outcomes have been demonstrated to occur, and may reflect the quality of care provided. Mortality is fundamental in quality of care assessment because of the importance of process-outcome links and its role as an outcome indicator of quality care. Validation of administrative datasets allow for future studies of heart failure outcomes using clinical data sources. Predictive models for heart failure mortality can be used to adjust for patient risk when evaluating variations in heart failure outcomes, and may be useful for stratification of mortality risk.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".