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 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 | 0.075 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.055 | 0.014 |
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".