How can we meet the information needs of patients with early stage papillary thyroid cancer considering radioactive iodine remnant ablation?
Bibliographic record
Abstract
In patients with early stage papillary thyroid carcinoma (PTC) who have had a thyroidectomy, the decision must be made to accept or reject radioactive iodine remnant ablation (RRA). Counselling patients about this decision can be challenging, given the medical evidence uncertainties and the complexity of related information. Although physicians are the primary source of medical information for patients considering RRA, some patients have a desire for supplemental information from sources such as the internet. Yet, thyroid cancer resources on the internet are of variable quality, and some may not be applicable to the individual case. We have developed a computerized educational tool [called a decision aid (DA)], directed to patients with early stage papillary thyroid cancer, and intended as an adjunct to physician counselling, to relay evidence-based medical information on disease prognosis and the choice to accept or reject RRA. DAs are tools used to inform patients about available treatment options and have been utilized in oncologic decision-making. We tested our web-based DA in fifty patients with early stage PTC and found that it improved medical knowledge. Furthermore, participants found the technical usability of the tool acceptable. We are currently conducting a randomized controlled trial comparing the use of the DA plus usual care to usual care alone to confirm the educational benefit of the website and examine its impact on the decision-making process. In the future, DAs may play an expanded role as an adjunct to physician counselling in the care of patients with thyroid cancer.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".