Comparison of ThinPrep and conventional smears in salivary gland fine-needle aspiration biopsies
Bibliographic record
Abstract
BACKGROUND: ThinPrep (TP) cytology for evaluation of nongynecological specimens is being increasingly used. There are few studies comparing TP with conventional smears (CS) in salivary gland (SG) fine-needle aspiration biopsies (FNAB). This study compares diagnostic accuracy and morphology of TP and CS in SG FNABs. METHODS: The authors retrospectively reviewed 98 satisfactory SG FNABs with both TP and CS. All cases had surgical resection. CS and TP slides were assessed for multiple morphological parameters, as well as the ability to make the diagnosis. Chi-square analysis was performed to compare CS and TP. RESULTS: An accurate diagnosis was rendered more commonly with CS compared with TP (57% versus 42%; P = .032), whereas the unsatisfactory rate was greater with TP compared with CS (19% versus 9%; P = .041). The error (4%) and indeterminate (35%) rates for TP were similar to CS. The diagnostic yield was greater for cellular cases, which were more frequent with CS compared with TP, than for cases of low cellularity; the diagnostic yield of cellular TP cases and cellular CS cases was similar. Artifacts (crush, air drying, obscuring blood) were more frequent (12%, 13%, and 27% versus 2%, 0%, and 1%; P <or= .006) in CS compared with TP. Although fragmentation was greater and nuclear detail was better in TP (P <or= .03), cell size was larger in CS (P = .002). A specific diagnosis of pleomorphic adenoma (PA) was more frequently rendered with CS compared with TP (83% versus 63%; P = .045). PA stroma was more abundant, and an epithelial-stromal interface (ESI) was more frequent in CS compared with TP (ESI, 76% versus 38%; P
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".