Do Lower-Risk Thyroid Cancer Patients Who Live in Regions with More Aggressive Treatments Have Better Outcomes?
Bibliographic record
Abstract
BACKGROUND: The management of differentiated thyroid cancer has traditionally consisted of total thyroidectomy with or without adjuvant radioactive iodine. However, in the last two decades, this approach has been challenged, with the consideration of more conservative approaches such as less radical surgery and deferring adjuvant treatment, especially in lower-risk patients. The objective of this study was to consider the effectiveness of current treatment options by comparing the survival outcomes from different geographic regions with different treatment philosophies. This study design was based on the concept of natural experiments in patient care that occur when physicians in different regions treat the spectrum of typical patients with varying treatments. METHOD: This population-based retrospective cohort study investigated 2444 patients with differentiated thyroid cancer ≤4 cm between 1990 and 2001 from Ontario, Canada. Extent of disease and extent of surgery were abstracted from pathology reports and were linked to downstream administrative medical information on treatments and outcomes. Patient demographics, tumor characteristics, treatments, and outcomes were compared between those geographic regions with more aggressive treatments and those regions with less aggressive treatments. RESULTS: Treatment varied across the province. When comparing outcomes in regions where patients had more extensive treatment to those in regions where patients had less extensive therapy, similar rates were found for 15-year survival, recurrence, and survival after recurrence. CONCLUSION: There were significant variations in treatment but no differences in outcomes for regions with more versus less aggressive approaches. These findings support the trend toward more conservative management approaches in the treatment of thyroid cancer.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".