Quality indicators for thyroid cancer surgery: current perspective
Bibliographic record
Abstract
INTRODUCTION: While the disease specific mortality of differentiated thyroid cancer has remained low with current treatments, its incidence has been steadily rising over the past several decades, and cancer related recurrence and morbidity have remained a significant problem. Quality indicators currently employed are relevant to the surgical intervention, but do not necessarily reflect oncological outcomes. Therefore, thyroid cancer specific surgical quality indicators, that offer insight into risk of cancer related morbidity and mortality are needed. AREAS COVERED: This review aims to discuss the role of measuring quality in thyroid surgical oncology and carry out a comprehensive review of potential quality indicators for thyroid cancer operations. The three quality indicators reviewed here are the postoperative radioactive iodine update by remnant thyroid tissue, the proportion of resected lymph nodes with evidence of metastases, and the post-operative serum thyroglobulin level. Expert commentary: Together, these quality indicators may be utilized to guide improvement of the quality of surgical care for this unique patient population. A critical future step in establishing the role of quality indicators for thyroid cancer surgery is the determination of cutoff values of each indicator in an evidence-based manner.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".