Pre‐op Thyroglobulin and Sentinel Lymph Node Biopsy Outcomes
Bibliographic record
Abstract
Objective 1) To retrospectively assess the usefulness of preoperative thyroglobulin (Tg) levels in predicting sentinel lymph node (SLN) biopsy (SLNB) status. 2) To evaluate the correlation between preoperative Tg levels and the overall number of positive SLNs. 3) To compare primary tumor (T) classification in patients according to SLNB outcome. Method Data from patients operated for well‐differentiated thyroid carcinoma (WDTC) at the McGill University Thyroid Cancer Center were collected from January 2007 to January 2012. Statistical analyses were performed using a Mann‐Whitney‐Wilcoxon test, a Pearson correlation coefficient and a Pearson χ2 test. Results Preoperative Tg levels and SLNB results were available in 74 patients (51 negative and 23 positive SLNBs). Mean preoperative Tg levels for negative and positive SLNB groups were 105.2 and 85.9 ng/mL, respectively, yielding no statistically significant difference (P =. 143). Moreover, no statistically significant correlation was found between Tg levels and the number of positive SLNs (P =. 515). While 82.4% of patients with negative SLNBs had a T1 or T2 class WDTC, 82.6% of patients with positive SLNBs had a T3 or higher class, yielding a statistically significant difference between the 2 groups (P <. 001). Conclusion Preoperative Tg levels are not significantly different in patients with positive SLNBs as compared to negative SLNBs, and show no significant correlation with the number of positive SLNs. Thus, an elevated preoperative Tg is not a predictor of SLN status. Patients with positive SLNBs, however, have significantly worse T classifications.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".