Interinstitutional variation in the use of abciximab for percutaneous coronary intervention.
Bibliographic record
Abstract
BACKGROUND: Several clinical trials have established abciximab as an efficacious agent for use in conjunction with percutaneous coronary intervention; however, there is little documented about its use in routine clinical practice in Canada. OBJECTIVES: To determine the use of abciximab, and secondarily, its associations with one-year death and repeat revascularization rates in 2751 Alberta residents who underwent percutaneous coronary intervention in 1999. METHODS: Descriptive statistics were used to determine use patterns. Logistic regression models were used to define risk of long-term outcomes and to determine associations between abciximab use and risk-adjusted death and repeat revascularization rates. RESULTS: Abciximab was administered to 43.5% of the study population and interinstitutional differences were revealed (site A, 46.7%; site B, 26.6%; site C, 54.6%, P<0.001). Use patterns according to the adjusted risk of death or repeat revascularization also differed across these sites. There were no differences between patients treated with versus those treated without abciximab in risk-adjusted one-year mortality (3.7%, 95% CI 2.8% to 3.7% versus 3.1%, 95% CI 2.3% to 4.0%) or revascularization rates (16.7%, 95% CI 14.8% to 19.1% versus 15.8%, 95% CI 14.0% to 17.7%). However, differences in baseline clinical characteristics between these two groups may limit the inferences that can be made from these outcome comparisons. CONCLUSIONS: Use patterns varied across the tertiary care hospitals in Alberta and the use of abciximab was not associated with reduced rates of long term death or repeat revascularization. The absence of provincial or national guidelines may have influenced the uptake and application of this novel therapy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".