Systemic Anti-Cancer Therapy Use in Palliative Care Outpatients With Advanced Cancer
Bibliographic record
Abstract
PURPOSE: To evaluate factors associated with continuation of systemic anti-cancer therapy (SACT) after palliative care consultation, and SACT administration in the last 30 days of life, in outpatients with cancer referred to palliative care. Timing of referral was of particular interest. METHODS: Patient, disease, and treatment-related factors associated with SACT before and after palliative care, and in the last 30 days of life, were identified using 3-level multinomial logistic regression. Referral to palliative care was categorized by time from death as early (>12 months), intermediate (6-12 months), and late (≤6 months). RESULTS: Of the 337 patients, 240 (71.2%) received SACT for advanced cancer; of these, 126 (52.5%) received SACT only prior to palliative care while 114 (47.5%) also received SACT afterward. Only 35/337 (10.4%) received SACT in the last 30 days of life. On multivariable analysis, factors associated with continuing SACT after palliative care consultation were a cancer diagnosis for <1 year (OR 3.09, p = 0.01), breast primary (OR 11.88, p = 0.0008), and early (OR 28.8, p < 0.001) or intermediate (OR 6.67, p < 0.001) referral timing. No factors were significantly associated with receiving SACT in the last 30 days versus earlier, but the median time from palliative care referral to death in those receiving SACT in the last 30 days versus stopping SACT earlier was 1.78 versus 4.27 months. CONCLUSION: Patients who received SACT following palliative care consultation were more likely to be referred early; however, patients receiving SACT in their last 30 days tended to be referred late.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".