Sensitivity, Specificity, and Posttest Probability of Parotid Fine‐Needle Aspiration
Bibliographic record
Abstract
OBJECTIVES: (1) To analyze the sensitivity and specificity of fine-needle aspiration (FNA) in distinguishing benign from malignant parotid disease. (2) To determine the anticipated posttest probability of malignancy and probability of nondiagnostic and indeterminate cytology with parotid FNA. DATA SOURCES: Independently corroborated computerized searches of PubMed, Embase, and Cochrane Central Register were performed. These were supplemented with manual searches and input from content experts. REVIEW METHODS: Inclusion/exclusion criteria specified diagnosis of parotid mass, intervention with both FNA and surgical excision, and enumeration of both cytologic and surgical histopathologic results. The primary outcomes were sensitivity, specificity, and posttest probability of malignancy. Heterogeneity was evaluated with the I(2) statistic. Meta-analysis was performed via a 2-level mixed logistic regression model. Bayesian nomograms were plotted via pooled likelihood ratios. RESULTS: The systematic review yielded 70 criterion-meeting studies, 63 of which contained data that allowed for computation of numerical outcomes (n = 5647 patients; level 2a) and consideration of meta-analysis. Subgroup analyses were performed in studies that were prospective, involved consecutive patients, described the FNA technique utilized, and used ultrasound guidance. The I(2) point estimate was >70% for all analyses, except within prospectively obtained and ultrasound-guided results. Among the prospective subgroup, the pooled analysis demonstrated a sensitivity of 0.882 (95% confidence interval [95% CI], 0.509-0.982) and a specificity of 0.995 (95% CI, 0.960-0.999). The probabilities of nondiagnostic and indeterminate cytology were 0.053 (95% CI, 0.030-0.075) and 0.147 (95% CI, 0.106-0.188), respectively. CONCLUSION: FNA has moderate sensitivity and high specificity in differentiating malignant from benign parotid lesions. Considerable heterogeneity is present among studies.
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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 | 0.052 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.025 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".