Thyroid cancer patients’ involvement in adjuvant radioactive iodine treatment decision-making and decision regret: an exploratory study
Bibliographic record
Abstract
PURPOSE: We explored regret in thyroid cancer patients, relating to the decision to accept or reject adjuvant radioactive iodine treatment. METHODS: We studied patients with a recent diagnosis of early stage papillary thyroid carcinoma, in whom treatment decisions on adjuvant radioactive iodine had been finalized. Participants completed a Decision Regret Scale questionnaire. We asked the participants to identify who made the final decision about radioactive iodine treatment. We explored the relationship between decision regret and a) degree of patient involvement in decision-making and b) receipt of radioactive iodine treatment. RESULTS: We included 44 individuals, more than half of whom received adjuvant radioactive iodine treatment (26/44). Decision regret was generally low (mean 22.1, standard deviation [SD] 13.0). Participants reported that the final treatment decision was made by the following: patient and doctor (52.3%, 23/44), completely the patient (27.3%, 12/44), or completely the physician (20.5%, 9/44). Decision regret significantly differed according to who made the final decision: the patient (mean 19.0, SD 11.3), patient and doctor (mean 19.5, SD 7.4), and the doctor (mean 32.9, SD 20.37) (F = 4.569; degrees of freedom = 2, 41; p = 0.016). There was no significant difference in decision regret between patients who received radioactive iodine and those who did not (mean difference -2.5; 95% confidence interval -10.6, 5.6; p = 0.540). CONCLUSION: Thyroid cancer patients who reported being involved in the final treatment decision on adjuvant radioactive iodine had less regret than those who did not.
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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 | 0.004 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".