Analysis of Nondiagnostic Results in a Large Series of Thyroid Fine-Needle Aspiration Cytology Performed over 9 Years in a Single Center
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it