Prospective Evaluation of the McGill Thyroid Nodule Score
Bibliographic record
Abstract
Objective 1) Familiarize the audience with the new McGill Thyroid Nodule Score (MTNS) scoring system for thyroid nodules. 2) Present the statistics on the MTNS and why we feel this is a great adjunct in the management of thyroid nodules. 3) Describe a new tool at the clinician’s disposal that will help better manage patients with thyroid nodules. Method This is a prospective study. A total of 245 consecutive patients were enrolled between July 2009 and March 2011. McGill Thyroid Nodule Score (MTNS), age, gender, and extent of surgery were recorded preoperatively. Final pathology was recorded postthyroidectomy. The percentage of malignancy was calculated for each MTNS score. Using Fisher exact test, the risk of malignancy for each group was compared to that anticipated based on the MTNS. We then used linear regression to plot a best fit curve for both the retrospective and prospective data and compared the 2. Results We found no statistically significant difference when comparing the risk of malignancy between the retrospective group originally used to create the MTNS and the prospective group used to validate this tool with P >. 05 for all groups. Regression analysis revealed linear curves and also showed no significant difference between both groups (P =. 35). Conclusion The MTNS system has been validated prospectively and is a unified and individualized tool to help clinicians with multiple aspects in the management of patients with thyroid nodules. This includes discussion with patients regarding the risk of their nodules harbouring malignancy, decisions regarding extent of surgery, and discussion between physicians.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".