Abstract PO4-16-09: Enhancing Research on Inflammatory Breast Cancer through Count Me In: Assessing the Accuracy of Self-Reported Diagnoses
Bibliographic record
Abstract
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.
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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 | 0.042 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".