What is the data-defined length for short time from diagnosis to death that is associated with a low rate of palliative care enrolment for persons with colorectal cancer?
Bibliographic record
Abstract
OBJECTIVE: Prior research has demonstrated that people who die shortly after receiving a cancer diagnosis are at increased risk for not being referred to palliative care. No previous studies have empirically derived the length of time between diagnosis and death associated with increased risk. The objective of this study was to identify the length of time between diagnosis and death associated with increased risk for non-enrolment in a palliative care programme. METHODS: Binary recursive partitioning was employed to derive the cut-point for the number of days from colorectal cancer diagnosis to death predictive of a high risk for non-enrolment in a palliative care programme in two health districts in Nova Scotia, Canada. The study included all adults (≥20 years) who were diagnosed with colorectal cancer in the two districts between 1 January 2001 and 31 December 2005 and who died between 1 January 2001 and 31 December 2008 (n=894). RESULTS: Individuals who died within 18.5 days following diagnosis were at highest risk for non-enrolment in palliative care. Of the 60 adults who died in <18.5 days, 16.7% were enrolled in a palliative care programme; of the 835 adults who died ≥18.5 days after diagnosis, 65.9% were enrolled. CONCLUSIONS: This data-driven approach may be used to define the short diagnosis-to-death time frame at which individuals are at increased risk for non-enrolment in palliative care programmes. This approach allows researchers to further investigate and compare empirically-derived cut-points that identify those who die quickly and are at risk of not receiving palliative care.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".