EPID-06. The Key LARGO study: Building an international resource for glioblastoma epidemiology
Bibliographic record
Abstract
Abstract Despite decades of epidemiological research, no glioblastoma risk factors have been identified that explain substantial risk. Prior studies often rely on single- or multi-center recruitment, which is costly and limits generalizability to the broader glioblastoma patient population. To address this and increase the diversity of glioblastoma patients in epidemiological research, we have launched the Key LARGO study (NCT06625684, Key Longitudinal Associations with Risk and Glioblastoma Outcomes), an ongoing observational study of glioblastoma patients and their caregivers. Key LARGO uses web-based enrollment and at-home biospecimen collection via mailed Tasso+ kits for self-collection of blood and serum. Participants also complete a detailed, online survey (available in English or Spanish) on life history and health-related quality of life (FACT-Br). Primary caregivers are enrolled to help report patient outcomes, including vital status. This study is a collaboration between the Preston Robert Tisch Brain Tumor Center, the Glioblastoma Research Organization, and StacheStrong. Recruitment for Key LARGO began on December 10, 2025. As of May 29, 2025, 79 patients and 30 caregivers have consented. Of these, 63 consented for blood collection, 53 received kits, and 32 (60%) have returned samples. Fifty-seven participants (72%) have completed the full study survey. Participants are 51% male, with a median age of 49 years (range 38-59) at time of diagnosis, that were diagnosed between 2007 and 2025. Most (96%) identified as white. Nine percent identified as Ashkenazi Jewish, and 5% identified as Hispanic or Latino. Blood collection kits have been returned from the United States, Canada, Italy, and the United Kingdom. Key LARGO demonstrates the feasibility of web-based recruitment and remote biospecimen collection in glioblastoma research and provides a scalable model for increasing geographic and demographic diversity in epidemiological studies.
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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 | 0.028 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.042 | 0.016 |
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".