Abstract PO4-16-09: Enhancing Research on Inflammatory Breast Cancer through Count Me In: Assessing the Accuracy of Self-Reported Diagnoses
Notice bibliographique
Résumé
Abstract Background: Inflammatory breast cancer (IBC) is a rare and aggressive form of breast cancer that relies on clinical identification of specific breast changes for diagnosis, in addition to pathological confirmation of invasive breast cancer. There is a clear need to increase participation of patients with IBC in research to better understand its clinical course and optimal treatment strategy. Count Me In (CMI) is a nonprofit research initiative that enables patients across the United States and Canada to accelerate cancer research by sharing their clinical data and biospecimens. The Metastatic Breast Cancer Project (MBCProject) was the first CMI initiative and is a prospective longitudinal cohort designed to capture these data from patients with metastatic breast cancer. Methods: We reviewed available medical records of patients participating in the MBCProject and who self-reported as having IBC. Records were reviewed by a study team member to identify documentation by a provider of an IBC diagnosis. For records without a documented IBC diagnosis, the clinical symptoms were assessed using a novel quantitative IBC scoring system (Mason G et al. BCRF 2022) currently being validated. Finally, records were also assessed by a physician for final determination of an IBC diagnosis. Records were classified as “concordant” or “not concordant”. Concordant is defined as “review of medical records confirms IBC using the unifying set of specific diagnostic criteria”. We desired the rate of concordant cases to be ≥90%; if ≤ 85% it would be considered as unacceptable to rely solely on patient self-report of IBC diagnosis in future research. Results: We reviewed records of 79 patients participating in the MBCProject who self-identified as having IBC and had medical records collected. Of these, 51 (64.5%) had IBC stated in providers’ notes. Of the remaining 28 patients, 6/28 met criteria for IBC using the new IBC diagnostic criteria, 17/28 didn’t have evidence of IBC based on the records available and 6/28 we were unable to make a final determination due to lack of records at the time of initial diagnosis. In total, 57/79 (72%; 95% CI 61-82%) patients had a concordant diagnosis. Conclusion: Patient self-report registries such as CMI are invaluable for the collection of clinical information and biospecimens for research of patients with rare diseases, however, a self-reported diagnosis of IBC may not be reliable. To improve the accurate identification of IBC, optimization of the questions asked to patients on these registries is warranted. This may include additional screening questions such as specific skin findings and timing of onset of symptoms. Focusing on patients with stage III IBC may also provide a better patient population to test this strategy. Citation Format: Elizabeth Troll, Sean Ryan, Virginia (Ginny) Mason, Mariesa D. Powell, Aditi Hazra, Nikhil Wagle, Mary McGillicuddy, Sara Tolaney, Meredith Regan, Filipa Lynce. Enhancing Research on Inflammatory Breast Cancer through Count Me In: Assessing the Accuracy of Self-Reported Diagnoses [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 PO4-16-09.
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 | 0,042 | 0,118 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».