Towards using administrative databases to measure population-based indicators of quality of end-of-life care: testing the methodology
Bibliographic record
Abstract
This study is concerned with methods to measure population-based indicators of quality end-of-life care. Using a retrospective cohort approach, we assessed the feasibility, validity and reliability of using administrative databases to measure quality indicators of end-of-life care in two Canadian provinces. The study sample consisted of all females who died of breast cancer between 1 January 1998 and 31 December 2002, in Nova Scotia or Ontario, Canada. From an initial list of 19 quality indicators selected from the literature, seven were determined to be fully measurable in both provinces. An additional seven indicators in one province and three in the other province were partially measurable. Tests comparing administrative and chart data show a high level of agreement with inter-rater reliability, confirming consistency in the chart abstraction process. Using administrative data is an efficient, population-based method to monitor quality of care which can compliment other methods, such as qualitative and purposefully collected clinical 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 | 0.508 | 0.624 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".