Decision aid on radioactive iodine treatment for early stage papillary thyroid cancer: update to study protocol with follow-up extension
Bibliographic record
Abstract
BACKGROUND: Patient decision aids (P-DAs) are used to inform patients about healthcare choices, but there is limited knowledge about their longer term effects, beyond the time period of decision-making. METHODS/DESIGN: We developed a computerized P-DA that explains the choice of radioactive iodine (RAI) adjuvant treatment or no RAI, for patients with low risk papillary thyroid cancer after total thyroidectomy. The original protocol for a randomized controlled trial, comparing the use of the P-DA (with usual care) to usual care alone, has been published in Trials http://www.trialsjournal.com/content/11/1/81. We found that P-DA (with usual care) significantly improved patients' medical knowledge at the time of decision-making (primary outcome) compared to usual care alone (control). In this update, we present the protocol for an extended follow-up study (15 to 23 months post-randomization), including qualitative and quantitative methods. The patient outcomes evaluated using quantitative questionnaires include: the degree to which patients feel well-informed about their RAI treatment choice, decision satisfaction, decision regret, cancer-related worry, mood, and trust in the treating physician. The qualitative component explores the experiences of RAI treatment decision-making, treatment satisfaction, and trial participation in a representative subgroup of patients. Extended follow-up study results will be described for the entire study population, and data will be compared between the P-DA and control groups. RESULT AND CONCLUSION: This mixed methods extended follow-up study will provide data on long term outcomes, relating to the use of a computerized P-DA in decision-making about adjuvant RAI treatment in early stage papillary thyroid cancer. DISCUSSION: Our results are intended to inform future research in this area, particularly relating to long term effects of the use of P-DAs in making healthcare choices. TRIAL REGISTRATION: Clinicaltrials.gov identifier NCT01083550, registered 24 February 2010 and last updated 5 January 2015.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".