Abstract PO3-28-01: More Than a Diagnosis: An Untold Story of Quality of Life
Notice bibliographique
Résumé
Abstract Background Advocates for Collaborative Education, a global coalition of patient, community, research, and policy advocates, initiated an anonymous, online survey to assess Quality of Life (QoL) impacts of cancer diagnosis and treatment to inform research, clinical care, and the broader cancer community. Identifying what matters most to individuals can potentially guide improvements in supportive care to effectively address QoL impacts. Methods A six-section, 93 question survey was co-created by patients, survivors, and advocates. Co-creation was instrumental in determining categories, specific questions, and measurement scales used to assess severity and impact of QoL challenges. Beyond demographic, treatment, and cancer diagnosis data, co-creation participants identified seventeen (17) treatment-related side effects. Survey questions asked about these side effects, their perceived severity, how well informed they felt about possibilities of experiencing these side effects, supportive care access, side effects of supportive care therapies, and personal preferences for receiving cancer-related information. Recruitment efforts for this online survey were conducted through advocacy groups and social media platforms. Results Across a wide range of demographic subsets, including age, race, stage of cancer, and treatment modalities, 334 respondents with a history of breast cancer reported a decline in QoL scores pre cancer vs post cancer according to the co-created measurement tool. Physical and emotional well-being emerged as top priorities for most respondents, 47.9% and 27.5% respectively. Of 17 listed side effects, respondents cited fatigue (49%), muscle/joint pain (36%), and emotional distress (35%) as primary concerns, with sexual dysfunction and cognitive impairment ranking prominently. Forty-six percent reported severe (frequent & highly disruptive) to very severe (constant & severely impacting quality of life) emotional distress, but only 55% of those felt informed or well informed about the possibility of emotional distress. Among respondents, only 41% received a plan to address emotional distress. Many felt unprepared for physical side effects as well. While 54% of individuals experienced diarrhea or fecal incontinence as a treatment side effect, 22% of affected individuals did not feel informed about the possibility of such side effects. Survey findings also highlight consequences of treatments to manage side effects. For example, 37% of respondents were prescribed treatment to address diarrhea and 100% of these patients reported additional side effects stemming from this supportive care including nausea, constipation, and fatigue. Detailed results for all categories as well as stratification by subsets of age, race, metastatic vs. early stage, treatment type, and others will be available for presentation and publication. Conclusion This study sheds light on an underexplored area of cancer research. Management of treatment side effects is integral to supportive care, yet supportive medications can introduce a cascade of their own side effects, exacerbating the overall burden on patients. Information and education are critical to bridge the gap. The collaboration with patients and survivors in developing this survey represents a pivotal step in determining areas that demand attention for improving QoL and offers valuable insights aimed at enhancing the well-being of individuals affected by cancer and additional research will pursue collaborative solutions. Demographic and High Level QoL Responses from MTAD (Breast) Detailed data on perceived severity, impact to QoL, perceived preparation for, and relevant supportive care is available for each side effect Citation Format: Stacey Tinianov, Kelly Shanahan, Catherine Thomsen, Ginny Mason, Laura Carfang, Joanne Mika Hayashi, Laurie Hutcheson. More Than a Diagnosis: An Untold Story of Quality of Life [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-28-01.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,007 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,005 |
| Communication savante | 0,005 | 0,007 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,003 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 ».