Opportunities and challenges for the development of “core outcome sets” in neuro-oncology
Bibliographic record
Abstract
Core Outcome Sets (COS) define minimum outcomes to be measured and reported in clinical effectiveness trials for a particular health condition/health area. Despite recognition as critical to clinical research design for other health areas, none have been developed for neuro-oncology. COS development projects should carefully consider: scope (how the COS should be used), stakeholders involved in development (including patients as both research partners and participants), and consensus methodologies used (typically a Delphi survey and consensus meeting), as well as dissemination plans. Developing COS for neuro-oncology is potentially challenging due to extensive tumor subclassification (including molecular stratification), different symptoms related to anatomical tumor location, and variation in treatment options. Development of a COS specific to tumor subtype, in a specific location, for a particular intervention may be too narrow and would be unlikely to be used. Equally, a COS that is applicable across a wider area of neuro-oncology may be too broad and therefore lack specificity. This review describes why and how a COS may be developed, and discusses challenges for their development, specific to neuro-oncology. The COS under development are briefly described, including: adult glioma, incidental/untreated meningioma, meningioma requiring intervention, and adverse events from surgical intervention for pediatric brain tumors.
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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 | 0.373 | 0.337 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".