EPID-31. EFFICIENT STUDY OF SURGICALLY TREATED MENINGIOMA THROUGH CLINICAL DATA STANDARDIZATION
Bibliographic record
Abstract
Abstract With increasing epidemiological and biological studies on meningiomas, standardized clinical data capture will be necessary to interpret results across studies and to translate them to clinical practice. To meet this need for consistent clinical data capture, the International Consortium on Meningioma, has developed a set of common data elements (CDEs), to be used across clinical and biological research. Building from terminology used in clinical practice and ongoing studies, we used a consensus-of-experts approach to arrive at a set of standardized and vetted data elements. Areas of focus include patient characteristics, patient history, radiological and intra-operative description, histological diagnosis and molecular testing, non-surgical interventions, and surveillance. These data elements are implemented in Research Electronic Data Capture (REDCap) forms. With REDCap implementation, components of the CDE set can be selected and instituted in a local REDCap instance for efficient study start-up. The scope of the current CDE set focuses on surgically treated meningioma. However, modular forms provide flexibility and to allow the scope to be expanded to include non-surgically treated meningioma. Dissemination plans are underway to introduce the CDEs to neuro-oncology researchers and clinicians through publication and presentations, where feedback will be solicited. Adoption of CDEs for research will improve consistency and interpretability across studies, expediting research in this frequently occurring tumor.
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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 | 0.172 | 0.307 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.010 |
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".