Decision Regret Following the Choice of Surgery or Active Surveillance for Small, Low-Risk Papillary Thyroid Cancer: A Prospective Cohort Study
Bibliographic record
Abstract
Background: It is important to understand cancer survivors' perceptions about their treatment decisions and quality of life. Methods: We performed a prospective observational cohort study of Canadian patients with small (<2 cm) low-risk papillary thyroid cancer (PTC) who were offered the choice of active surveillance (AS) or surgery ( Clinicaltrials.gov NCT03271892). Participants completed a questionnaire one year after their treatment decision. The primary intention-to-treat analysis compared the mean decision regret scale total score between patients who chose AS or surgery. A secondary analysis examined one-year decision regret score according to treatment status. Secondary outcomes included quality of life, mood, fear of disease progression, and body image perception. We adjusted for age, sex, and follow-up duration in linear regression analyses. Results: The overall questionnaire response rate was 95.5% (191/200). The initial treatment choices of respondents were AS 79.1% (151/191) and surgery 20.9% (40/191). The mean age was 53 years (standard deviation [SD] 15 years) and 77% (147/191) were females. In the AS group, 7.3% (11/151) of patients crossed over to definitive treatment (two for disease progression) before the time of questionnaire completion. The mean level of decision regret did not differ significantly between patients who chose AS (mean 22.4, SD 13.9) or surgery (mean 20.9, SD 12.2) in crude ( p = 0.730) or adjusted ( p = 0.29) analyses. However, the adjusted level of decision regret was significantly higher in patients who initially chose AS and crossed over to surgery (beta coefficient 10.1 [confidence interval; CI 1.3–18.9], p = 0.02), compared with those remaining under AS. In secondary adjusted analyses, respondents who chose surgery reported that symptoms related to their cancer or its treatment interfered with life to a greater extent than those who chose AS ( p = 0.02), but there were no significant group differences in the levels of depression, anxiety, fear of disease progression, or overall body image perception. Conclusions: In this study of patients with small, low-risk PTC, the mean level of decision regret pertaining to the initial disease management choice was relatively low after one year and it did not differ significantly for respondents who chose AS or surgery.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".