A novel classification system for research reporting in rare and progressive genetic conditions
Bibliographic record
Abstract
AIM: To create a classification system for severe, rare, and progressive genetic conditions for use in research reporting. METHOD: A modified Delphi consensus technique was used to create and reach agreement on a new system of condition categories. Interrater reliability was tested via two rounds of an online survey whereby physicians classified a subset of conditions using our novel system. Overall percentage agreement and agreement above chance were calculated using Fleiss' kappa (κ). RESULTS: Eleven physicians completed the first Delphi, with an overall agreement of 76.4%, the κ value was 0.57 (95% confidence interval 0.51-0.63), indicating moderate agreement (0.41-0.60) above chance. Based on the first survey several categories were described in more detail. The second survey confirmed a classification system with 12 categories, with an overall percentage agreement among the participants of 82.6%. The overall mean κ value was 0.71 (95% confidence interval 0.65-0.77), indicating substantial agreement (0.61-0.80). INTERPRETATION: Our new system was useful in categorizing a broad range of rare childhood diseases and may be applicable to other rare disease studies; further validation in larger cohorts is required. WHAT THIS PAPER ADDS: This novel 12-category classification system can be used in research reporting in rare and progressive genetic conditions.
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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 | 0.084 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".