Thyroid cancer quality of care indicators: A scoping review
Bibliographic record
Abstract
BACKGROUND: Thyroid cancer, the most common endocrine malignancy, has highly variable practice patterns. This scoping review aimed to identify quantitative and qualitative quality of care indicators (QIs) essential for providing optimal care in thyroid cancer management. METHODS: A comprehensive search across MEDLINE, EMBASE, PubMed, and Web of Science identified QIs defining structures, processes, and outcomes in five care phases: pre-diagnosis, diagnosis, treatment, post-treatment surveillance, and end-of-life care. RESULTS: Of the 3,143 articles screened, 36 were included, yielding 135 unique QIs. Key diagnostic QIs were the use of a standardized ultrasound reporting system (n = 4), diagnostic fine needle aspiration biopsy (FNAB) (n = 3), and FNA cytology reporting with the Bethesda System (n = 3). Common treatment QIs included thyroidectomy by high-volume surgeons (≥10-32 cases/year) (n = 7), preoperative voice assessment for high-risk patients (n = 4), and recurrent laryngeal nerve monitoring (n = 3). Serum thyroglobulin (Tg) monitoring was the primary post-treatment QI for recurrence (n = 2). CONCLUSIONS: Developing an evidence-based QI list can identify care gaps, direct targeted interventions, promote care standardization, and improve outcomes for thyroid cancer patients.
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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 | 0.020 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.032 | 0.038 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".