Current surgical treatment of intermediate risk differentiated thyroid cancer: a systematic review
Bibliographic record
Abstract
Introduction: Surgical treatment of thyroid cancer has become less aggressive but for many patients, the threshold for performing total thyroidectomy (TT), as opposed to thyroid lobectomy (TL), has remained unclear. Current American Thyroid Association (ATA) guidelines encourage more individualization of treatment options, which necessitates explicit review of the pros and cons of the different options with patients.Areas covered: This review focuses on the extent of surgery for treatment of intermediate-risk differentiated thyroid cancer, restricted to relevant literature available after publication of the 2015 ATA guidelines.Expert opinion: Dynamic risk-stratification facilitates a tailored approach when deciding on the extent of surgery for thyroid cancer. Treatment with TT allows for a lower recurrence risk, a simpler follow-up regimen, and treatment with adjuvant post-operative radioactive iodine. Treatment with TL has a lower associated risk of complications and avoidance of lifelong thyroid hormone replacement but has a significant risk of requiring a completion thyroid lobectomy (CT). Overall, treatment with TL and TT have comparable survival outcomes, but TL is the more cost-effective option. Larger cancer size is correlated with worse clinical outcomes, and numerous subgroup analyses have shown poorer outcomes for cancers with a diameter that is 2–4 cm compared to 1–2 cm.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".