Value of thyroid incidentalomas on positron emission tomographic scans among thyroidectomy patients.
Bibliographic record
Abstract
OBJECTIVES: To evaluate the preoperative predictive value of a positive positron emission tomographic (PET) scan with respect to malignancy in future thyroidectomy candidates, particularly when the fine-needle aspiration biopsy (FNAB) results in indeterminate findings, and to establish the efficiency with which this can be incorporated as a preoperative marker and potentially contribute to a standardized scoring system for thyroid nodule patients. METHODS: This retrospective study examined 1048 thyroidectomy patients, of whom 45 underwent PET with computed tomography for unrelated reasons, among which 13 results were focally positive. The final pathology was evaluated and compared to this result to determine the correlation. RESULTS: All patients with positive PET results were shown postthyroidectomy to have a thyroid malignancy (13 of 13), corresponding to a positive predictive value of 100%. There was no correlation between a negative PET scan and malignancy, however. When integrating the PET scan criteria in the McGill Scoring System, 4 of these 13 were shifted into a high chance of malignancy group, allowing a more accurate assessment of their risk than they might have previously received. CONCLUSION: In comparison with previous data, our results indicate a strong relationship between a positive PET scan and malignancy. If available and used in conjunction with the other preoperative diagnostic tools (outlined by the McGill Thyroid Nodule Scoring System), this test can hold significant merit in determining a therapeutic strategy, particularly in the face of an indeterminate FNAB.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".