Stratification of intermediate-risk fine-needle aspiration biopsies.
Bibliographic record
Abstract
OBJECTIVE: The goal of our study was to identify factors in intermediate-risk fine-needle aspiration (FNA) results that are predictive of malignancy. DESIGN: Retrospective chart review. SETTING: Head and neck oncology clinic at the London Health Sciences Centre. METHODS: A database of 665 patients who had received thyroid surgery between 2001 and 2007 was created. FNA biopsy data were collected for each patient, as well as pathologic, patient, and ultrasound data. Of the 665 patients, 302 FNA biopsies were considered intermediate risk, and these data were analyzed. MAIN OUTCOME MEASURE: Presence of malignancy. RESULTS: Intermediate-risk patients were significantly more likely to have a benign nodule if the width to length (W/L) ratio of their nodule was < 0.6. The relative risk was 5.64 (95% confidence interval [CI] 0.81-39.65) (p < .05). As well, patients who were in the intermediate-risk category were significantly more likely to have a malignancy if they were < 40 years old compared to those patients who were > or = 40 years old. CONCLUSIONS: Both age and W/L ratio of a nodule can be used to help predict whether a nodule in an intermediate-risk patient is malignant. An intermediate-risk patient who has a W/L ratio < 0.6 can be treated conservatively based on the extremely low risk of malignancy (2.86%).
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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 | 0.005 |
| 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) | 0.001 | 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".