The Incidental Thyroid Lesion in Parathyroid Disease Management
Bibliographic record
Abstract
OBJECTIVE: The incidental thyroid lesion is a common finding during general imaging studies. Their management has been the subject of numerous studies and recommendations. Parathyroid disease workup necessitates imaging investigation of the adjacent thyroid gland and therefore provides a unique window to the perioperative management of thyroid incidentaloma. The specific prevalence of incidental thyroid lesions in the context of parathyroid disease is unknown. We seek to investigate its prevalence during parathyroid workup and surgery and to ascertain if there was a change in management of these patients. STUDY DESIGN: Five-year retrospective database review. SETTING: Tertiary care medical center. SUBJECTS AND METHODS: The source and indication for referral, preoperative investigation findings, and management of the incidental thyroid lesions were examined. The actual procedure performed and final pathology results were assessed. RESULTS: A total of 98 patients and 106 operations, including revision surgeries, were identified. There were 21 incidental thyroid lesions (21.4%) detected, whereby 15 patients underwent fine-needle aspirations and 12 subsequently had diagnostic hemithyroidectomies. This decision was made preoperatively in 5 patients and intraoperatively in 7 patients at the time of parathyroid surgery. Along with other pathologies, there were 7 patients with micropapillary thyroid carcinoma identified. CONCLUSIONS: In our series, the prevalence of incidental thyroid lesion and thyroid malignancy is comparable to the general population. The management of the initial parathyroid disease in our patients was altered by the imaging and cytological findings of these thyroid lesions. This has implications on perioperative counseling of the thyroid and parathyroid disease.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".