Identification in Administrative Databases of Women Dying of Breast Cancer
Bibliographic record
Abstract
PURPOSE: Palliative care is an essential component of cancer care, and population-based research is needed to monitor its impact. Administrative databases are the cornerstone of health services research. Their limitation is that cause of death is not sufficient to readily classify decedents as terminally ill for the study of the health services they received at the end of life. The study purpose is to develop and test the validity of an algorithm allowing the classification of the decedents as dying of breast cancer (BC), using administrative data. METHODS: Validation was carried out through a chart review of 119 BC decedents extracted from hospital-based databases. This algorithm was applied to 3,384 deceased women with BC representative of the whole population. The effect of the classification by the algorithm was illustrated by the shift in the distributions of age and place of death. RESULTS: The validation showed a sensitivity of 95%, a specificity of 89%, a positive predictive value of 98%, and negative predictive value of 77% for the classification of women dying of BC. Of the 3,384 decedents, 2,293 were classified as dying of, and 1,091 as not dying of BC. Women dying of BC were younger, died less often at home (6.9% v 17.9%), and in chronic care institutions (4.1% v 14.8%), and more often in acute-care beds (69.9% v 57.1%). CONCLUSION: This novel way to classify decedents is conceptually based and empirically validated through chart review and impact on distribution of age and place of death.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".