Abstract PO-269: Documentation methods for vulvar pain: Implications for detection of vulvar cancer, a cancer with known disparities
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,025 | 0,065 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,032 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».