Reprocessing Liquid-Based Pap Smears Using Glacial Acetic Acid and Clinician Education: Impact on Quality of Care
Bibliographic record
Abstract
Unsatisfactory Papanicolaou (Pap) smears frequently result from insufficient cellularity owing to a poor sample collection technique and are a significant cause of repeat patient visits. Reprocessing of unsatisfactory Pap smears has been shown to increase cell yield and enhance the detection of abnormalities. Owing to an increasing unsatisfactory rate in our laboratory, we sought to improve quality with a combined strategy of reprocessing and clinician education. We applied a combined strategy of reprocessing using glacial acetic acid to all unsatisfactory Pap smears and those with borderline cellularity in conjunction with educating health care providers about appropriate Pap smear collection to reduce the unsatisfactory rates within our institution during a study period of 1 year. Clinician education consisted of distributing printed reference information and delivering oral presentations. Quality assurance data were collected at the end of the study period and compared with data obtained from the previous year. Use of lubricants and overrotation of the spatula during Pap smear collection were identified as the main causes of unsatisfactory specimens. Reprocessing reduced the overall unsatisfactory rate from 5.8% (598/10,354) to 2.6% (281/10,663). The reprocessing rate was 9.7% and did not decrease following implementation of educational initiatives. The cost of reagents used for reprocessing during the study period was $10,172 CAD. Our results support that reprocessing unsatisfactory Pap smears using glacial acetic acid can reduce the number of unsatisfactory Pap smears but increases the cost incurred by the laboratory. Further intervention at the point of specimen collection is required to maximize the benefit and reduce the cost of reprocessing.
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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 | 0.004 | 0.034 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".