Quality indicators for end-of-life breast cancer care: Testing the use of administrative databases
Bibliographic record
Abstract
6066 Background: Defining, measuring and monitoring quality of care is a facet of health services research that is growing in importance. Breast cancer offers a disease model to examine quality end-of-life (EOL) care provided to women. Administrative data have the unique potential to provide population-based measures of quality of care. The objective of this study was to assess the feasibility of using routinely-collected administrative data to measure quality EOL care for breast cancer patients. Methods: A cohort of all women in Nova Scotia who died of breast cancer between 01/01/1998 and 31/12/2002 was assembled from the Cancer Registry and Vital Statistics data. The EOL study period was defined as the last 6 months of life. A total of 864 women met the eligibility criteria. After a literature review, an expert panel identified 19 indicators that were potentially measurable through administrative data. Physician billings, hospital discharge abstracts and seniors pharmacare data, supplemented by clinical datasets, were utilized to calculate the statistics with which to represent the indicators. Results: Benchmark measures of care across the cohort show 63.4% died in a hospital, a mean continuity of care index of 0.786, and the mean number of inpatient days in the last 30 was 9.9. Indicators of aggressive care include 9.3% had chemotherapy in the last 14 days, 5.6% had more than 1 emergency room visit in the last 30 days, and 29.1% had more than 14 inpatient days in the last 30 days. Conclusions: Weaknesses of using these data include: 1) fixed variables with an administrative rather than a clinical objective; 2) lack of comprehensiveness of various datasets; and 3) the use of billings data where increasingly physicians are paid through methods other than fee-for-service. Strengths of this approach are: 1) population-based cohort; 2) comprehensiveness of cohort selection through the provincial Vital Statistics file; and 3) accessibility of data. No significant financial relationships to disclose.
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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 | 0.138 | 0.393 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".