Decision Making for the Extent of Thyroidectomy in the Patient With Atypical Cytologic Results
Bibliographic record
Abstract
OBJECTIVE: to identify additional preoperative factors that could reliably be used to aid in determining the appropriate extent of thyroidectomy. DESIGN: retrospective chart review. SETTING: tertiary care academic hospital. PATIENTS: two hundred consecutively treated patients who underwent thyroid surgery after having a fine-needle aspiration biopsy procedure yielding a specimen that met the criteria for atypical cytologic features. MAIN OUTCOME MEASURE: final histopathologic diagnosis of malignant vs benign disease. RESULTS: the final diagnosis was benign in 42.5% of patients and malignant in 57.5%. The presence of microcalcifications within the nodule on ultrasonography (US) was significantly associated with a higher risk of malignant disease (relative risk = 1.31, P = .04). When examined individually, age, sex, family history of thyroid malignant disease, exposure to head and neck irradiation, nodule size, rim enhancement on US, and intranodular vascularity on US were not significantly associated with an increased risk of malignant disease. Mulivariate stepwise logistic regression modeling was used to identify a model that could reliably predict a higher probability of malignant disease. The final model determined that patients with microcalcifications on US and a nodule of 2.0 cm or larger had a 74.3% risk of malignant disease vs a 47.5% risk in patients with no microcalcifications and a nodule smaller than 2.0 cm. This difference was statistically significant. When the predicted probabilities of malignant disease were compared with the observed probabilities, the goodness-of-fit test revealed no significant difference (P = .95). CONCLUSION: microcalcifications and nodule size can be used to risk-stratify patients with an atypical fine-needle aspiration biopsy result and aid in determining the appropriate extent of thyroidectomy.
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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 | 0.015 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".