Using Poisson Regression to Compare Rates of Unsatisfactory Pap Smears Among Gynecologists and to Evaluate a Performance Improvement Plan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To evaluate impact of a performance improvement (PI) plan implemented after initial analysis, comparing 7 gynecologists working in 2 clinics. STUDY DESIGN: From January to October 2005, unsatisfactory rates for gynecologists and clinics were calculated. A PI plan was instituted at the end of the first quarter of 2006. Unsatisfactory rates for each quarter of 2006 and the first quarter of 2007 were calculated. Poisson regression analyzed results. A total of 100 ThinPrep Pap smears initially deemed unsatisfactory underwent reprocessing and revaluation. RESULTS: The study's first part evaluated 2890 smears. Clinic unsatisfactory rates, 2.7% and 2.6%, were similar (p > 0.05). Gynecologists' unsatisfactory rates were 4.8-0.6%; differences between each of the highest 2 and lowest rates were significant (p < 0.05). The study's second part evaluated 13,453 smears. Unsatisfactory rates by quarter were 1.76%, 0.67%, 0.38%, 0.70% and 0.76%; the first quarter's rate was higher than in other quarters. For the study's third part, 13 of 100 reprocessed smears remained unsatisfactory. CONCLUSION: Gynecologists' unsatisfactory rates can differ. Analyzing differences can result in quality improvement. Reprocessing ThinPrep smears is an important means of reducing unsatisfactory rates but should not be a substitute for attention to quality in sampling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it