Variation in Routine Follow-Up Care After Curative Treatment for Head-and-Neck Cancer: A Population-Based Study in Ontario
Bibliographic record
Abstract
Background: The actual practices of routine follow-up after curative treatment for head-and-neck cancer are unknown, and existing guidelines are not evidence-based. Methods: This retrospective population-based study used administrative data to describe 5 years of routine follow-up care in 3975 head-and-neck cancer patients diagnosed between 2007 and 2012 in Ontario. Results: The mean number of visits per year declined during the follow-up period (from 7.8 to 1.9, p < 0.001). The proportion of patients receiving visits in concordance with guidelines ranged from 80% to 45% depending on the follow-up year. In at least 50% of patients, 1 head, neck, or chest imaging test was performed in the first follow-up year; that proportion subsequently declined (p < 0.001). Factors associated with follow-up practices included comorbidity, tumour site, treatment, geographic region, and physician specialty (p < 0.05). Conclusions: Given current practice variation and the absence of an evidence-based standard, the challenge in identifying a single optimal follow-up strategy might be better addressed with a harmonized approach to providing individualized follow-up care.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".