Does the Frequency of Routine Follow-Up after Curative Treatment for Head-and-Neck Cancer Affect Survival?
Bibliographic record
Abstract
Background: Routine follow-up is a cornerstone of oncology practice, but evidence to support most aspects of follow-up is lacking. Our objective was to investigate the relationship between frequency of routine follow-up and survival. Methods: This population-based study used electronic health care data relating to 5310 patients from Ontario diagnosed with squamous-cell head-and-neck cancer during 2007-2012. Treatments included surgery (24.6%), radiotherapy with or without chemotherapy (52.4%), and combined surgery and radiotherapy (23%). We determined the oncologist who was following each patient after treatment; calculated the average follow-up visits to the oncologist during the subsequent 2.5 years for all patients who were doing well; and used Kaplan-Meier and multiple variable regression analysis to compare, by treatment, overall survival for patients in the high, typical, and low follow-up oncologist groups. Results: Many oncologists saw patients 40%-80% more often than other oncologists did. No relationship of appointment frequency with survival was observed for patients in any treatment group. Conclusions: The practice of routine follow-up varies and is costly both to a health care system and to patients. Without evidence about the effectiveness of current policies, further research is required to investigate new or optimal practices.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".