Decision aid on radioactive iodine treatment for early stage papillary thyroid cancer - a randomized controlled trial
Bibliographic record
Abstract
BACKGROUND: Patients with early stage papillary thyroid carcinoma (PTC), are faced with the decision to either to accept or reject adjuvant radioactive iodine (RAI) treatment after thryroidectomy. This decision is often difficult because of conflicting reports of RAI treatment benefit and medical evidence uncertainty due to the lack of long-term randomized controlled trials. METHODS: We report the protocol for a parallel, 2-arm, randomized trial comparing an intervention group exposed to a computerized decision aid (DA) relative to a control group receiving usual care. The DA explains the options of adjuvant radioactive iodine or no adjuvant radioactive iodine, as well as associated potential benefits, risks, and follow-up implications. Potentially eligible adult PTC patient participants will include: English-speaking individuals who have had recent thyroidectomy, and whose primary tumor was 1 to 4 cm in diameter, with no known metastases to lymph nodes or distant sites, with no other worrisome features, and who have not received RAI treatment for thyroid cancer. We will measure the effect of the DA on the following patient outcomes: a) knowledge about PTC and RAI treatment, b) decisional conflict, c) decisional regret, d) client satisfaction with information received about RAI treatment, and e) the final decision to accept or reject adjuvant RAI treatment and rationale. DISCUSSION: This trial will provide evidence of feasibility and efficacy of the use of a computerized DA in explaining complex issues relating to decision making about adjuvant RAI treatment in early stage PTC. TRIAL REGISTRATION: Clinical Trials.gov Identifier: NCT01083550.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".