Thyroid cancer patient perceptions of radioactive iodine treatment choice: Follow‐up from a decision‐aid randomized trial
Bibliographic record
Abstract
BACKGROUND: Patient decision aids (P-DAs) inform medical decision making, but longer term effects are unknown. This article describes extended follow-up from a thyroid cancer treatment P-DA trial. METHODS: In this single-center, parallel-design randomized controlled trial conducted at a Canadian tertiary/quaternary care center, early-stage thyroid cancer patients from a P-DA trial were contacted 15 to 23 months after randomization/radioactive iodine (RAI) decision making to evaluate longer term outcomes. It was previously reported that the use of the computerized P-DA in thyroid cancer patients considering postsurgical RAI treatment significantly improved medical knowledge in comparison with usual care alone. The P-DA and control groups were compared for the following outcomes: feeling informed about the RAI treatment choice, decision satisfaction, decision regret, cancer-related worry, and physician trust. In a subgroup of 20 participants, in-depth interviews were conducted for a qualitative analysis. RESULTS: Ninety-five percent (70 of 74) of the original population enrolled in follow-up at a mean of 17.1 months after randomization. P-DA users perceived themselves to be significantly more 1) informed about the treatment choice (P = .008), 2) aware of options (P = .009), 3) knowledgeable about treatment benefits (P = .020), and 4) knowledgeable about treatment risks/side effects (P = .001) in comparison with controls. There were no significant group differences in decision satisfaction (P = .142), decision regret (P = .199), cancer-related worry (P = .645), mood (P = .211), or physician trust (P = .764). In the qualitative analysis, the P-DA was perceived to have increased patient knowledge and confidence in decision making. CONCLUSIONS: The P-DA improved cancer survivors' actual and long-term perceived medical knowledge with no adverse effects. More research on the long-term outcomes of P-DA use is needed.
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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 | 0.026 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".