A Comparison of Cervical Cancer Screening Rates among Women with Traumatic Spinal Cord Injury and the General Population
Bibliographic record
Abstract
BACKGROUND: Previous qualitative and survey studies have suggested women with spinal cord injury (SCI) are screened less often for cervical cancer compared with the general population. We investigated whether cervical cancer screening rates differ between population-based women with and without traumatic SCI, matched for age and geography. METHODS: A double cohort design was used, comparing women with SCI to the general population (1:4) using administrative data for Ontario, Canada. Women with SCI, identified using the Discharge Abstract Database for the fiscal years 1995-1996 to 2001-2002, were female residents of Ontario between the ages of 25 and 66, admitted to an acute care facility with a traumatic SCI (ICD-9 CM code 806 or 952). Women in the general Ontario population were randomly matched by age and geography. Screening rates were calculated from fee codes related to Papanicolaou (Pap) smear tests for a 3-year period preinjury and postinjury. RESULTS: There were 339 women with SCI matched to 1506 women in the general Ontario population. Screening rates pre-SCI were 55% for women with SCI and 57% during this same time period for matched women in the general population; post-SCI rates were 58% for both the two groups. Factors predicting the likelihood of receiving a Pap test for SCI cases included younger age and higher socioeconomic status. CONCLUSIONS: Utilization data suggest that there are no significant differences in screening rates for women with SCI compared with the general population. However, screening rates for women with SCI were significantly influenced by age as well as income.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
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".