Can preoperative thyroglobulin antibody levels be used as a marker for well differentiated thyroid cancer?
Bibliographic record
Abstract
BACKGROUND: It has been reported that thyroglobulin antibody are more frequently elevated in patients with thyroid cancercompared to general population. This study aims at evaluating whether preoperative thyroglobulin antibody (TgAb) levels increase the likelihood that a thyroid nodule is malignant. METHODS: A retrospective review of 586 patients who underwent thyroidectomy was conducted. Demographic data, TgAb levels, and final histopathology were recorded. Patients were divided into two groups: TgAb positive (defined as TgAb ≥ 30 IU/ml) and TgAb low/negative (defined as TgAb < 30). RESULTS: Preoperative TgAb levels were available in 405 patients. There were 353 (87 %) patients in the TgAblow/negative group (malignancy rate: 50.42 %) and 52 (13 %) 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 for thyroid malignancy were 16.04 %, 90.67 %, 65.38 % and 49.58 %, respectively. The relative risk of having a malignant thyroid nodule when the TgAb titers were≥30 IU/ml was 1.30 (CI1.04-1.62) and the odds ratio was 1.86 (CI 1.01-3.41). Both the Pearson chi-square test (p = 0.024) and Fisher's exact test (p = 0.017) yielded statistical significance between the two groups. CONCLUSIONS: In this study, patients with preoperative TgAb ≥ 30 IU/ml had a higher rate of malignancy when compared topatients with TgAb < 30 IU/ml. This suggests that an elevated TgAb level may indicate that a thyroid nodule is at an increased risk for malignancy.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".