Operationalizing Outpatient Palliative Care Referral Criteria in Lung Cancer Patients: A Population-Based Cohort Study Using Health Administrative Data
Bibliographic record
Abstract
Background: Early referral of cancer patients for palliative care significantly improves the quality of life. It is not clear which patients can benefit from an early referral, and when the referral should occur. A Delphi Panel study proposed 11 major criteria for an outpatient palliative care referral. Objective: To operationalize major Delphi criteria in a cohort of lung cancer patients, using a prospective approach, by linking health administrative data. Design: Population-based observational cohort study. Setting/Subjects: The study population comprised 38,851 cases of lung cancer in the Ontario Cancer Registry, diagnosed from January 1, 2012, to December 31, 2016. Measurements: We operationalized 6 of the 11 major criteria (4 diagnosis or prognosis based and 2 symptom based). Patients were considered eligible (index event) for palliative care if they qualified for any criterion. Among eligible patients, we identified those who received palliative care. Results: Twenty-eight thousand one hundred sixty-four patients were eligible for palliative care by qualifying for either the diagnosis- or prognosis-based criteria ( n = 21,036, 76.5%), or for symptom-based criteria ( n = 7128, 23.5%). A total of 23,199 (82.4%) patients received palliative care. The median time from palliative care eligibility to the receipt of first palliative care or death or maximum study follow-up was 56 days (range = 17–348). Conclusions: We operationalized six major criteria that identified the majority of lung cancer patients who were eligible for palliative care. Most eligible patients received the palliative care before death. Future research is warranted to test these criteria in other cancer populations.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".