Are solitary thyroid nodules more likely to be malignant?
Bibliographic record
Abstract
BACKGROUND: Traditional teaching demonstrated that solitary thyroid nodules were more likely to be malignant. Newer studies show that there is no clear answer regarding the influence of the number or the distribution of nodules on the risk of malignancy. OBJECTIVES: The purpose of this study was to establish whether patients undergoing thyroid surgery and presenting with a solitary thyroid nodule show a greater rate of malignancy when compared to those presenting with multiple nodules. The secondary goal was to evaluate the impact of the distribution of the nodules (multiple unilateral nodules versus bilateral nodules) on the rate of malignancy in this population. METHOD: Retrospective review of the medical records of the 656 patients who underwent thyroidectomy at one of the hospitals of the McGill University Thyroid Cancer Centre between July 2006 and April 2011 was conducted. RESULTS: There was no significant difference in the malignancy rate between patients with a solitary nodule and patients with two to six thyroid nodules at ultrasonography, between patients with unilateral nodule(s) and patients with bilateral nodules, or between patients with at least one nodule > 1.0 cm and patients without any nodules > 1.0 cm (p = .870, .578, and .361, respectively). CONCLUSION: This study demonstrates that the likelihood of thyroid cancer is independent of the number of thyroid nodules. Moreover, our data show that the malignancy rate is not influenced by the distribution of the nodules or their size.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".