Impact of a reporting template on thyroid fine needle aspiration cytology reporting and cytohistologic concordance
Bibliographic record
Abstract
BACKGROUND: Reporting templates are increasingly common in all fields of pathology. In this paper, we present an assessment of the impact of a thyroid fine needle aspiration cytology (FNAC) template on diagnostic classification and cytohistologic concordance. MATERIALS AND METHODS: A thyroid FNAC reporting template was developed and introduced at a university teaching hospital. We examined FNAC reports for a five-month period before introduction of the template and compared these to the five month period after the template introduction. We recorded diagnostic categorization as well as cytohistologic correlation. RESULTS: A total of 168 cases were identified in the five month period prior to the introduction of the reporting template and 172 cases in the five month period after the introduction of the reporting template. The template appeared to improve the diagnostic precision of benign conditions without altering the proportion of cases classified as unsatisfactory, benign or abnormal. There was no significant difference in the rate of cytohistologic concordance before and after the template introduction. CONCLUSIONS: The introduction of a reporting template for thyroid FNAC improved diagnostic precision of benign conditions and did not alter the general diagnostic classification or cytohistologic concordance.
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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 | 0.046 | 0.209 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".