Acetic Acid Recovery of Gynecologic Liquid-Based Samples of Apparent Low Squamous Cellularity
Bibliographic record
Abstract
OBJECTIVE: To characterize cervicovaginal cytology samples with < 5,000 squamous cells on the initial ThinPrep slide (Cytyc Corp., Boxborough, Massachusetts, U.S.A) and to attempt sample recovery using acetic acid. STUDY DESIGN: Cervicovaginal cytology samples with <5,000 squamous cells on the original ThinPrep slide and residuum were reprocessed by adding 3 mL of 3:1 CytoLyt (Cytyc)/glacial acetic acid with production of a second slide. Both slides were reviewed for squamous cell quantitation and the presence of background material and abnormal cells. RESULTS: From a total of 1,833 cases, 147 (8.0%) were identified for reprocessing; 71 (48.3%) were grossly bloody and 58 (39.4%) grossly cloudy. Reprocessing resulted in a second slide with > 5,000 squamous cells in 116 (78.9%) cases and was most effective on cloudy samples (89.7% recovery) and bloody samples (71.8% recovery). Abnormal cells were identified in 13 (8.9%) reprocessed samples. In all but 2 cases the abnormal cells were present on the initial slide and demonstrated the same degree of abnormality as the reprocessed slide but were fewer in number. CONCLUSION: Acetic acid recovery increases squamous cell recovery when initially inadequate, reducing the number of unsatisfactory cases and in rare cases identifying a cytologically significant lesion not apparent on the original slide.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".