Quantitative assessment of nuclear grooves in fine needle aspirates of the thyroid: a retrospective cytohistologic study of 94 cases.
Bibliographic record
Abstract
OBJECTIVE: To quantify the association between the percentage of nuclear grooves and papillary thyroid carcinoma (PTC) in fine needle aspirates (FNAs) of the thyroid. STUDY DESIGN: All the thyroid FNA cases (n = 1,775) received at the McGill University Health Center Cytopathology Laboratory in 2003 and 2004 were retrieved. The 94 diagnostic FNAs with histologic followup were selected. The percentage of nuclear grooves was quantified manually by counting the number of grooves in 300 cells, at x400, in the areas where the nuclear grooves were most frequent. Using descriptive statistics and graphs, we evaluated the association between the percentage of nuclear grooving and 2 outcomes of surgical diagnosis. RESULTS: The mean percentage of nuclear grooves was 23.7% in PTC vs. 9.44% in non-PTC cases. We identified the cut-off of 20% nuclear grooving as having the optimal positive predictive value of 80% for presence of PTC and negative predictive value of 77% for absence of PTC. CONCLUSION: The presence of nuclear grooves in > or = 20% of cells, as counted in selective fields where grooves are the most frequent, is highly predictive of PTC.
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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 | 0.004 |
| 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 | 0.001 |
| 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".