Cervical cancer screening in Louisiana Urban population: A retrospective chart review to assess the value of SCM (Self-pap)
Bibliographic record
Abstract
The standard technique for cervical cancer screening in the USA is to conduct a Papanicolaou (Pap) smear. In recent years, however, self-collection methods (SCMs), such as BD Onclarity, have gained increasing popularity in an effort to improve the ease and access of screening for patients. The objective of this study is to assess whether screening for cervical cancer was influenced by utilizing SCMs in the primary care clinic. To assess the effect of SCMs, the screening rates at seven different primary care clinics in the Northwest Louisiana urban environment were reviewed and analyzed. Results were collected from Q1 of 2022 to Q1 of 2025. A Poisson model with a log offset for the number of patients seen per quarter was used to model the number of patients who had completed cervical cancer screening. An interaction term was used to examine the change in rates of cervical cancer screening before and after implementation of the SCM. It was found that screening improved on a collective basis for all the clinics during the time period examined. Of note, the proportion of individuals screened improved by 4% each quarter before the intervention and by 2% each quarter after the intervention. When comparing screening at the various clinics individually, it was found that there was no significant difference in the rate of screening when modeling for the period as a whole. When modeling for just the time period after SCM introduction, a difference of moderate significance was noted between certain clinics, although the reason for this finding is unknown. SCMs may have had a positive impact on screening rates. Further research to characterize the method of screening and traits of the screened and unscreened patients could yield important information to promote successful population healthcare initiatives in the future.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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".