Relation between surgical oncologic quality indicators for papillary thyroid cancer
Bibliographic record
Abstract
Background: It remains unclear how thyroid surgical oncologic quality indicators (TSOQIs) are related to each other, and how to best interpret and apply these measures within the context of surgical quality assurance. We aimed to examine the relation between 3 TSOQIs: postoperative serum thyroglobulin level, 24-hour radioactive iodine uptake (RAIU) and metastatic lymph node ratio (MLNR). Methods: We conducted a retrospective review of patients who underwent total thyroidectomy for treatment of papillary thyroid cancer (PTC) performed by a single high-volume thyroid surgeon at a tertiary referral centre between 2012 and 2017. To establish the strength of correlation between pairs of quality indicators and the MACIS (metastasis, age, completeness of resection, invasion and size) prognostic score, we performed tests of normality and used the Spearman correlation coefficient to determine the correlation of nonnormal data containing outliers. Results: A total of 139 patients with PTC were included in the study. Their mean MACIS score was 5.0 (standard deviation 1.5). Fifteen patients had high-risk thyroid cancer (MACIS score > 6.99). A weak correlation was found between serum thyroglobulin level and RAIU (rs = 0.27, p = 0.006) and a moderate correlation was found between serum thyroglobulin level and MLNR (rs = 0.40 p = 0.002). A weak correlation between serum thyroglobulin level and MACIS score was also observed (rs = 0.20, p = 0.05). Conclusion: Based on our findings, we propose that the postoperative serum thyroglobulin level represents the quality metric that has the most clinical utility because it is measurable in all patients and also correlates with both RAIU and MLNR. With further research, surgeons seeking to evaluate the oncologic quality of thyroidectomy performed for PTC may consider applying a quality indicator to their future practice.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".