Patterns of health care utilization preceding a colorectal cancer diagnosis are strong predictors of dying quickly following diagnosis
Bibliographic record
Abstract
BACKGROUND: Understanding the predictors of a quick death following diagnosis may improve timely access to palliative care. The objective of this study was to explore whether factors in the 24 months prior to a colorectal cancer (CRC) diagnosis predict a quick death post-diagnosis. METHODS: Data were from a longitudinal study of all adult persons diagnosed with CRC in Nova Scotia, Canada, from 01Jan2001-31Dec2005. This study included all persons who died of any cause by 31Dec2010, except those who died within 30 days of CRC surgery (n = 1885 decedents). Classification and regression tree models were used to explore predictors of time from diagnosis to death for the following time intervals: 2, 4, 6, 8, 12, and 26 weeks from diagnosis to death. All models were performed with and without stage at diagnosis as a predictor variable. Clinico-demographic and health service utilization data in the 24 months pre-diagnosis were provided via linked administrative databases. RESULTS: The strongest, most consistent predictors of dying within 2, 4, 6, and 8 weeks of CRC diagnosis were related to health services utilization in the 24 months prior to diagnosis: i.e., number of specialist visits, number of days spent in hospital, and number of family physician visits. Stage at diagnosis was the strongest predictor of dying within 12 and 26 weeks of diagnosis. CONCLUSIONS: Identifying potential predictors of a short timeframe between cancer diagnosis and death may aid in the development of strategies to facilitate timely and appropriate referral to palliative care upon a cancer diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".