Risk factors for well‐differentiated thyroid carcinoma in patients with thyroid nodular disease
Bibliographic record
Abstract
OBJECTIVES: Evaluate current accepted risk factors for well-differentiated thyroid carcinoma, and develop a predictive model to determine one's risk of malignancy given a thyroid nodule. STUDY DESIGN: Retrospective analysis of 600 patients. SUBJECTS AND METHODS: Patients with benign thyroid nodular disease and with well-differentiated thyroid cancer were randomly selected. Patient, clinical, and investigational data were compared by means of univariate and multivariate regression analyses. RESULTS: Age, regional lymphadenopathy, ipsilateral vocal cord palsy, solid and/or calcified nodules, and an aspiration biopsy being malignant or suspicious predicted for cancer (P < 0.05). Regional lymphadenopathy and vocal cord palsy are perfect predictors of malignancy. Multivariate analysis indicated age, solid and/or calcified nodules, and all fine-needle aspiration biopsy results to be significant in assessing risk (P < 0.05). CONCLUSION: Taking individual risk factors in isolation is not always reliable. Using a predictive model, one can anticipate a patient's risk of malignancy when the diagnosis is unclear.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".