Randomized Controlled Trial of a Computerized Decision Aid on Adjuvant Radioactive Iodine Treatment for Patients With Early-Stage Papillary Thyroid Cancer
Bibliographic record
Abstract
PURPOSE: Decision-making on adjuvant radioactive iodine (RAI) treatment for early-stage papillary thyroid cancer (PTC) is complex because of uncertainties in medical evidence. Using a parallel, two-arm, randomized, controlled trial design, we examined the impact of a patient-directed computerized decision aid (DA) on the medical knowledge and decisional conflict in patients with early-stage PTC considering the choice of being treated with adjuvant RAI or not. The DA describes the rationale, possible risks and benefits, and the medical evidence uncertainty relating to the choice. PATIENTS AND METHODS: We recruited 74 patients with early-stage PTC after thyroidectomy. Participants were assigned by using 1:1 central computerized randomization to either the DA group with usual care (intervention) or usual care alone (control). Medical knowledge about PTC and RAI treatment (the primary outcome), as well as decisional conflict (a secondary outcome), were measured by using validated questionnaires, and the respective scores were compared between groups. RESULTS: Consistent with PTC epidemiology, 83.8% (62 of 74) of the participants were women, and the mean age was 45.8 years (range, 19 to 79 years). Medical knowledge about PTC and RAI treatment was significantly greater and decisional conflict was significantly reduced in the DA group compared with the control group (respective P values < .001). The use of adjuvant RAI treatment was not significantly different between groups (DA group, 11 of 37 [29.7%]; controls, seven of 37 [18.9%]; P = .278). CONCLUSION: A computerized DA improves informed decision making in patients with early-stage PTC who are considering adjuvant RAI treatment. DAs are useful for patients facing decisions subject to medical evidence uncertainty.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Randomized trial | high |
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".