Preoperative serum thyroglobulin as an adjunct to fine-needle aspiration in predicting well-differentiated thyroid cancer.
Bibliographic record
Abstract
BACKGROUND/PURPOSE: when fine-needle aspiration biopsy (FNAB) of a thyroid nodule yields indeterminate pathology, management decisions become complex, and other preoperative predictors of thyroid cancer must be employed to assess the risk of malignancy. Although thyroglobulin (Tg) is currently accepted as the serum marker of choice in the detection of well-differentiated thyroid cancer (WDTC) recurrence, its preoperative role in the workup of a thyroid nodule remains controversial. The purpose of this study was to evaluate the potential role for Tg as a preoperative indicator of primary WDTC, specifically in patients with indeterminate FNAB. METHODS: this was a retrospective review of 861 consecutive thyroidectomy patients; 297 patients had indeterminate FNAB, of which 68 had serum levels of Tg measured prior to surgery. The predictive value of various threshold levels of preoperative Tg for WDTC was evaluated. Patients with nonindeterminate FNAB or final pathology containing medullary carcinoma, anaplastic carcinoma, or lymphoma were excluded. RESULTS: eighty-one percent (25 of 31) of patients with both indeterminate FNAB and preoperative Tg ≥ 75 microg/L had well-differentiated cancer on final pathology compared to 58% (172 of 297) of patients with indeterminate cytology alone (p = .014, RR = 1.4). In addition, mean preoperative Tg levels were found to be significantly higher in patients with WDTC compared to those with benign pathology (223 vs 53 microg/L, p = .007). DISCUSSION/CONCLUSION: our results imply that elevated preoperative serum Tg levels may be predictive of WDTC. This marker may be useful as an aid when making management decisions in patients with indeterminate cytology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".