Healthcare contact days for people with stage IV non-small cell lung cancer (NSCLC) in Ontario: A population-based study
Bibliographic record
Abstract
BackgroundSurvival is limited with advanced NSCLC, and frequent healthcare visits can become all-consuming. We investigated patterns of contact days—days with any in-person healthcare contact as a measure of potential time toxicity—in a population-based sample. MethodsWe created a population-based, retrospective cohort with health administrative data from Ontario, Canada, of adults with stage IV NSCLC in 2014-2017, dying 2014-2019. The primary outcome was contact days assessed from diagnosis to death. We stratified analyses by receipt, type and lines of systemic therapy administered. We plotted and fitted with cubic splines the weekly percentage of contact days to obtain trajectories over the disease course. ResultsWe identified 5,785 stage IV NSCLC patients. The median (interquartile range [IQR]) survival was 108 days (49-426), and median percentage of contact days was 33.3%. Patients receiving systemic treatment had longer median survival (261 [152-420] vs. 66 [34-130] days) and lower median percentage of contact days (22.2% vs. 40.9%). Overall and for subgroups (systemic therapy vs. not; type and lines of therapy), trajectories followed a U-shaped distribution, with highest rates immediately following diagnosis and prior to death. The difference between the maximal peak and trough was greater in patients who received systemic therapy (peak 34.8% vs. trough 15.9%, ""deeper U"") vs. not (39.5% vs. 27.6%, ""shallower U""). ConclusionStage IV NSCLC patients spent a significant proportion of days alive with healthcare contact. These data serve as a call to recognize patient time burdens, improve care efficiency, and better support patients during periods of high burden.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".