McGill Thyroid Nodule Score (MTNS): "rating the risk," a novel predictive scheme for cancer risk determination.
Bibliographic record
Abstract
OBJECTIVE: There are presently a great number of publications pertaining to the clinical risk factors associated with thyroid cancer. These studies deal mostly with a single feature from either patient demographics, physical examination, laboratory values, imaging, or cytology. We sought to create a novel scoring system that integrates the diagnostic indices of each of these clinical features for carcinoma. METHODS: A retrospective analysis of 1047 consecutive thyroidectomy patients was performed. Each patient was assigned a cancer risk score according to a newly devised 22-variable scoring scheme termed the McGill Thyroid Nodule Score (MTNS). The MTNS was developed by a multidisciplinary team of endocrinologists, thyroid surgeons, and pathologists using already established evidence-based risk factors for thyroid cancer. RESULTS: The exact incidence of malignancy was calculated for each MTNS score based on final pathology. The incidence for scores of 1 to 3 was 27%, of 4 to 7 was 32%, of 8 was 39%, of 9 to 11 was 63%, of 12 to 13 was 88%, and of 14 to 18 was 93%. All (130 of 130) patients with a score ≥ 19 had carcinoma. A score ≤ 8 correlated with a 32% (115 of 357) risk of thyroid cancer, whereas a score > 8 implied an 86% (417 of 487) risk. CONCLUSION: Our data suggest that a combined scoring system, the MTNS, can serve as an accurate predictor of the risk for thyroid cancer in a specific thyroid nodule. This will help physicians better formulate management decisions accordingly.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".