FINE NEEDLE ASPIRATION CYTOLOGY OF NECK LESION- AN EXPERIENCE AT TERTIARY CARE HOSPITAL IN CENTRAL GUJARAT
Bibliographic record
Abstract
Introduction: Fine needle aspiration cytology has become a rapid, cost effective investigative method for obtaining reliable tissue diagnosis especially for the sites like neck where considerable overlapping of various structures makes it difficult to reach to exact diagnosis. Objective: Present study was taken up to evaluate role of FNA in management and diagnosis of various neck lesions and to compare FNA with conventional biopsy for providing correct tissue diagnosis. Method: Total 641 cases of neck lesions were subjected to FNA and out of these, 71 were further subjected to conventional surgical biopsy and results were correlated histologically. FNACs were performed in outpatient department of a tertiary care hospital by 23-24 gauge needle and 10 ml syringe. Results: Out of total 591 satisfactory smears, there were 140 thyroid lesions, 20 salivary gland lesions, 400 lymphnode lesions, 31 cystic lesions of neck. The overall sensitivity, specificity, accuracy, positive predictive value and negative predictive value of FNA for neck lesions were 93.1%, 100%, 98.4%, 90.1% & 100% respectively. Conclusion: Thus, this study concludes that FNAC is quite sensitive, specific, accurate investigative procedure with very good patient compliance. Use of FNAC should be encouraged as an investigation for initial diagnostic evaluation of neck lesions and as a tool to avoid unnecessary surgical procedures and its complications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".