Preoperative TSH and thyroglobulin levels: would it predict thyroid cancer?
Bibliographic record
Abstract
OBJECTIVE: The goal of this study is to determine whether preoperative TSH and Tg levels can be used as predictors of thyroid cancer. STUDY DESIGN: Retrospective chart review. METHODS: Charts of patients who had undergone thyroid surgery between 2006 and 2012 were subjected to review. Demographic data, preoperative TSH and Tg levels, and final histopathological results were recorded. Patients were divided depending on preoperative TSH and Tg levels. Group 1 consisted of patients with elevated TSH and Tg, Group 2 had elevated TSH only, Group 3 - elevated Tg only, and in Group 4 neither TSH nor Tg were elevated. RESULTS: 653 patient charts were reviewed and 386 patients were excluded due to incomplete information. 212 patients were female. Mean age was 50 years. Group 1 included 52 patients, 25 of them (48%) had well-differentiated thyroid cancer (WDTC). Relative risk was 1.59 and the odds ratio amounted to 1.79. Group 2 included 80 patients, 36 (45%) of whom had WDTC. Group 3 consisted of 58 patients, 23 (39.6%) of them with WDTC. Group 4 comprised 77 patients, where WDTC was present in 16 (20.8%) cases. CONCLUSION: TSH and Tg levels can aid in preoperative assessment of a thyroid nodule.
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| 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".