Cystic masses of the lateral neck: Diagnostic value comparison between fine‐needle aspiration, core‐needle biopsy, and frozen section
Bibliographic record
Abstract
BACKGROUND: The usefulness of fine-needle aspiration (FNA), core-needle biopsy (CNB), and frozen section (FS) for assessing lateral cystic neck masses (LCNM) remains unclear. METHODS: A retrospective review of patients presenting with a LCNM was undertaken. RESULTS: In total, 135 patients were included. FNA had a lower sensitivity then CNB (59% vs 83%; P = .036) and FS (59% vs 93%; P = .01). FS had a better negative predictive value (NPV) when compared to FNA (92% vs 40%; P < .001) and CNB (92% vs 50%; P = .062). Positive predictive values (PPV) and sensitivities were similar among all groups. CONCLUSION: Given its adequate PPV (92%), FNA should be used initially on LCNM. Because of its high sensitivity, CNB should be considered if FNA is not diagnostic of malignancy. FS should always follow a CNB indicative of malignancy, because of low NPV. A diagnosis of malignancy on FNA, CNB, or FS strongly indicates presence of malignancy.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".