Analysis of Nondiagnostic Results in a Large Series of Thyroid Fine-Needle Aspiration Cytology Performed over 9 Years in a Single Center
Bibliographic record
Abstract
OBJECTIVE: Thyroid fine-needle aspiration cytology (FNAC) is the most valuable, cost-effective and accurate method for the evaluation of patients with thyroid nodules. One of its limitations is that up to 20% of results are nondiagnostic or unsatisfactory. The aim of this study was to analyze the number of thyroid FNAC specimens with nondiagnostic results obtained on an outpatient basis and how many of these had to be repeated according to their results. STUDY DESIGN: This was a retrospective analysis of diagnostic reports of nondiagnostic thyroid FNAC specimens obtained between 1 January 2004 and 31 December 2012 which were retrieved by means of a computerized search. The FNAC results and the age and sex of the patients were collected. RESULTS: From a total of 15,292 thyroid FNAC specimens, 6.8% (n = 1,033) corresponded to nondiagnostic cases. Eligible diagnostic reports for analysis included 877 cases (106 were repetitions of previous nondiagnostic FNAC). After an initial nondiagnostic finding for 771 FNAC smears, 29.5% (n = 225) were repeated with the following results: 43.6% insufficient, 49.3% benign, 6.2% follicular neoplasm, 0.4% suspicious for malignancy and 0.4% malignant. Twenty-two patients underwent a second repeated FNAC. Here the findings were: 36.4% insufficient, 59.1% benign, 4.5% follicular neoplasm, 0.0% suspicious for malignancy and 0.0% malignant. CONCLUSIONS: There was a low rate of repeated FNAC among the group of nondiagnostic cases. With repeated FNAC, the rate of nondiagnostic cases and the number of results that potentially demand surgery diminish.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".