Abstract PO-269: Documentation methods for vulvar pain: Implications for detection of vulvar cancer, a cancer with known disparities
Bibliographic record
Abstract
Abstract Introduction. Vulvar pain can be a symptom of vulvar cancer, which is frequently detected at late stage for elderly women, and has racial disparities. Better documentation, detection, and monitoring of vulvar pain, may eventually lead to early detection of vulvar cancer to reduce age and racial disparities in this cancer. Unfortunately, typical body outline drawings for measuring pain location do not allow documentation of the specific details of vulvar pain. Our study purpose was to compare two measures of pain location for patients with another vulvar pain condition, vulvodynia. Methodology. Using baseline data from an ongoing randomized clinical trial conducted in Chicago, 62 women with vulvodynia marked their pain on a genital specific outline and 59 of those also marked their pain on a full body outline. The women’s ages ranged from 20-62 years (31.8±9.4 years). Their education levels were high school (10%), some college (9%), bachelors degree (43%), and advanced leveled education (38%). 11% were Asian, 7% were Black, 74% were White, and 9% were other race. They completed PAINReportIt, an electronic version of the McGill Pain Questionnaire, which allowed women to mark the region of their pain and describe their pain intensity. ImageJ software was used to isolate and analyze the surface area included within the markings on a full body outline with a total of 48 segments and on a genital area outline with a total of 22 segments. Descriptive statistics and Pearson’s correlation were used to analyze the number of segments marked and the number of pixels marked on each segment of both outlines. Results. As a context for women’s pain experience, the average pain intensity was 3.9±2.6. On the full body outline, 41 women marked the genital area only contrary to 18 women who marked both the genital area and elsewhere. On the full body outline, 24/48 segments were marked and the most frequent were segments near the vulvar region: left anterior thigh (n=59), right anterior thigh (n=54), left lumbar/iliac region (n=49), and right lumbar/iliac region (n=47). Some women also marked regions in the gluteal and posterior thigh region: left gluteal region (n=13), right gluteal region (n=9), and left hamstrings (n=10). On the genital specific outline, 22/22 body areas were marked. The most frequent vulvar specific areas marked were: vestibular fossa (n=51), left labia minora (n= 52)and vagina (n=55). There was a moderate correlation (r=.43, p=.001) between the body surface area marked on the full-body outline and the body surface area marked on the genital area outline. Conclusion. Study findings support the validity of the body surface area as a measure of pain location using either outline. The genital area outline provides more specific information about pain in the vulvar region, and implications for its use have the potential to eliminate disparities of vulvar cancer through the improved detection and monitoring of vulvar pain. Citation Format: Guettchina Telisnor, Rishabh Garg, Yingwei Yao, Judith Schlaeger, Diana J. Wilkie. Documentation methods for vulvar pain: Implications for detection of vulvar cancer, a cancer with known disparities [abstract]. In: Proceedings of the AACR Virtual Conference: Thirteenth AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2020 Oct 2-4. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2020;29(12 Suppl):Abstract nr PO-269.
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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 | 0.025 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 0.002 |
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".