Meeting abstracts from the 11th edition of the European conference on Rare Diseases & Orphan Products (ECRD) 2022
Bibliographic record
Abstract
Background: The European Rare Disease Research Coordination and Support Action (ERICA) consortium aims at promoting and disseminating the adoption of standardized PROMs for rare diseases.The dedicated working team (the WP3 group) facilitates the creation of a PROMs repository for use in clinical practice, evaluation of care and clinical research.Material and methods: First, among the 4.000 questionnaires described in the Mapi Research Trust PROMs database, PROQOLID, we have selected PROMs developed and validated for rare diseasesand PROMs measuring specific functional impacts (such as mobility, self-care or communication).Second, we conducted a survey among European Reference Networks and patient organisations to collect additional PROMs of interest.Third, we developed coding rules for PROMs, based on the International Classification of Functioning, Disability and Health (ICF) code.The resulting ICF-coded PROMs had to match the ICF-coded functional impacts of rare diseases, generated by semi-structured interviews with the Orphanet Disability Questionnaire.Results: The search in PROQOLID identified 279 PROMs developed in rare diseases and 200 PROMs measuring functional impacts.The survey identified 31 additional PROMs.A preliminary coding was conducted on a convenient sample of 10 PROMs including generic (EQ-5D, SF-36), disease specific (Myasthenia Gravis-Quality of Life (MG-QOL) and Myasthenia Gravis-Activity of Daily Living (MG-ADL)) and function specific (Health Assessment Questionnaire (HAQ), National Eye Institute Visual Function Questionnaire -25 (NEI-VFQ-25)) PROMs. Conclusion:We have selected a first set of more than 500 PROMs eligible for inclusion in the repository.We have defined and tested PROMs coding rules, which will allow 600 rare diseases described through the Orphanet Disability Questionnaire to be matched with the relevant PROMs.The next steps are to implement the coding model to the full set of PROMs, to operationalize the repository platform for both researchers and clinicians and to routinely code new eligible PROMs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.447 | 0.263 |
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".