Regional Variation across Canadian Centers in Radioiodine Administration for Thyroid Remnant Ablation in Well-Differentiated Thyroid Cancer Diagnosed in 2000–2010
Bibliographic record
Abstract
Background. Use of radioactive iodine (RAI) ablation has been reported to vary significantly between studies. We explored variation in RAI ablation care patterns between seven thyroid cancer treatment centers in Canada.Methods. The Canadian Collaborative Network for Cancer of the Thyroid (CANNECT) is a collaborative registry to describe and analyze patterns of care for thyroid cancer. We analyzed data from seven participating centers on RAI ablation in patients diagnosed with well-differentiated (papillary and follicular) thyroid cancer between 2000 and 2010. We compared RAI ablation protocols including indications (based on TNM staging), preparation protocols, and administered dose. We excluded patients with known distant metastases at time of RAI ablation.Results. We included 3072 patients. There were no significant differences in TNM stage over time. RAI use increased in earlier years and then declined. The fraction of patients receiving RAI varied significantly between centers, ranging between 20–85% for T1, 44–100% for T2, 58–100% for T3, and 59–100% for T4. There were significant differences in the RAI doses between centers. Finally, there was major variation in the use of thyroid hormone withdrawal or rhTSH for preparation of RAI ablation.Conclusion. Our study identified significant variation in use of RAI for ablation in patients with well-differentiated thyroid cancer both between Canadian centers and over time.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".