Trends in 1-year survival of people admitted to hospital in Ontario, 1994–2009
Bibliographic record
Abstract
BACKGROUND: Changes in the long-term survival of people admitted to hospital is unknown. This study examined trends in 1-year survival of patients admitted to hospital adjusted for improved survival in the general population. METHODS: One-year survival after admission to hospital was determined for all adults admitted to hospital in Ontario in 1994, 1999, 2004, or 2009 by linking to vital statistics datasets. Annual survival in the general population was determined from life tables for Ontario. RESULTS: Between 1994 and 2009, hospital use decreased (from 8.8% to 6.3% of the general adult population per year), whereas crude 1-year mortality among people with hospital admissions increased (from 9.2% to 11.6%). During this time, patients in hospital became significantly older (median age increased from 51 to 58 yr) and sicker (the proportion with a Charlson comorbidity index score of 0 decreased from 68.2% to 60.0%), and were more acutely ill on admission (elective admissions decreased from 47.4% to 42.0%; proportion brought to hospital by ambulance increased from 16.1% to 24.8%). Compared with 1994, the adjusted odds ratio (OR) for death at 1 year in 2009 was 0.78 (95% confidence interval [CI] 0.77-0.79). However, 1-year risk of death in the general population decreased by 24% during the same time. After adjusting for improved survival in the general population, risk of death at 1 year for people admitted to hospital remained significantly lower in 2009 than in 1994 (adjusted relative excess risk 0.81, 95% CI 0.80-0.82). INTERPRETATION: After accounting for both the increased burden of patient sickness and improved survival in the general population, 1-year survival for people admitted to hospital increased significantly from 1994 to 2009. The reasons for this improvement cannot be determined from these data.
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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 | 0.000 | 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) | 0.002 | 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".