Role of FNAC in Evaluation of Neck Masses
Bibliographic record
Abstract
Background: Despite fine needle aspiration cytology (FNAC) has a well recognized role in investigation neck masses, insufficient cellular yield in FNA remain an issue. The aim of this study is to evaluate the effectiveness of FNAC as a primary investigation tool in neck masses. Methods: This is a retrospective study of 47 cases with FNAC performed from February 2008 till June 2009 at Otorhinolaryngology Department, Ampang Hospital. The result were compared with results of 44 patients with neck masses and subjected for excision/incision biopsy as first line investigation of tissue diagnosis during the same period of time. Results: A total of 37 out of 47 patients with FNAC have a conclusive result (78.7% diagnostic yield). Inflammatory lesions (46%) are the common pathology followed by benign lesions (41%) and malignant lesions (13%). Excision/incision biopsy have 100% tissue diagnostic yield with 48% were haematological malignancies followed by reactive lymphadenopathy (27%), tuberculosis lymphadenopathy (16%) and metastatic carcinoma (9%). Conclusion: Despite lower tissue diagnostic yield compared to excision/incision biopsy, FNAC remains a safe and appropriate first line investigation of neck masses. doi: http://dx.doi.org/10.4021/jcs178e
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".