Intraoperative Frozen-Section Analysis for Thyroid Nodules
Bibliographic record
Abstract
OBJECTIVES: To determine accuracy and intertest agreement of preoperative fine-needle aspiration cytology (FNAC) and intraoperative frozen-section analysis (FS) findings in thyroid surgery, and to assess the influence of intraoperative FS findings on decision making and the utility of FS in thyroid surgery. DESIGN: Retrospective analysis. The results of preoperative FNAC, intraoperative FS, and final histopathological analyses were taken from the histopathology reports. We calculated intertest agreement using the kappa statistic. PATIENTS: Two-hundred fifteen patients who underwent primary thyroid surgery. All patients were treated by the same surgeon (S.J.W.). RESULTS: T he sensitivity and specificity of FNAC were 57.4% and 91.7%, respectively. The sensitivity and specificity of FS were 32.4% and 96.5%, respectively. The intertest agreement was poor (kappa = 0.17). In case of malignant FNAC findings, the FS result did not influence treatment decisions; in case of a malignant FS result on the background of a benign, indeterminate, or nondiagnostic FNAC finding, the FS result influenced treatment decisions in 88% of cases. CONCLUSIONS: Intraoperative FS did not give additional information in cases where a malignant neoplasm was predicted by the FNAC finding. In this setting, it led to conflicting results and did not contribute to correct decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| 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 | 0.001 |
| 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".