Can Preoperative Thyroglobulin Antibody Levels Be Used as a Marker for Well‐Differentiated Thyroid Cancer?
Bibliographic record
Abstract
Objectives: Evaluate whether the presence of preoperative thyroglobulin antibody (TgAb) levels can help predict the final pathology of thyroid nodules. Assess whether higher levels of preoperative TgAb increase the likelihood that a thyroid nodule is malignant. Methods: A retrospective chart review of patients who underwent thyroidectomy in 3 McGill University‐affiliated hospitals between January 2012 and 2014 was conducted. Demographic data, TgAb levels, and final histopathology were recorded. Patients were divided into 2 groups: TgAb positive (defined as TgAb ≥30 IU/mL) and TgAb low/negative (defined as TgAb <30). Micropapillary thyroid carcinomas were considered to be benign. These data were then statistically analyzed using SPSS. Results: Preoperative TgAb levels were available in 412 patients. There were 360 patients in the TgAb low/negative group (malignancy rate: 51.39%) and 52 patients in the TgAb positive group (malignancy rate: 65.38%). The sensitivity, specificity, positive predictive value, and negative predictive value of TgAb ≥30 IU/mL as a diagnostic test for thyroid malignancy were 15.53% (confidence interval [CI] 11.00‐21.01), 90.67% (CI 85.66‐94.38), 65.38% (50.91‐78.03), and 48.61% (CI 43.34‐53.91), respectively. The relative risk was 1.2723 (CI 1.0192‐1.5883) and the odds ratio was 1.7868 (CI 0.9732‐3.2804). Both the Pearson chi‐square test ( P =. 024) and Fisher's exact test ( P =. 017) yielded statistical significance between the 2 groups. Conclusions: Our study demonstrates that patients with preoperative TgAb ≥30 IU/mL had a higher rate of malignancy when compared to patients with TgAb <30 IU/mL. This suggests that an elevated TgAb level may increase the risk that a thyroid nodule is malignant.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 teacher head, 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".