P.029 Relevance and home completion rate of patient reported outcome measures set in chronic inflammatory neuropathies
Bibliographic record
Abstract
Background: Patient-centred care is important in the management of chronic inflammatory neuropathies (CIN) given the heterogeneity in disease course and treatment response. Patient Reported Outcome Measures (PROMs) support value-based healthcare by aligning treatment goals with what matters most to patients. This study evaluated the relevance of PROMs to patients and the feasibility of use in clinical management. Methods: PROMs assessing quality of life, pain, fatigue, and overall disability were collected prospectively from 32 patients with CIN every three months over a 12-month period. Patients provided feedback on relevance of the measures. PROMs were sent electronically prior to the visit. Results: Completion rate was 92%. Home vs. in-clinic completion increased from 56% to 85% over the course of the study. There was an association between completion of the panel and perceived relevance. The PROMs were consistently rated as highly relevant, with disability and fatigue measures rated highest. Conclusions: PROMs are appraised as highly relevant among patients with CIDP and MMN. Patients require support initially but adapt to electronically delivered home completion of questionnaires. We recommend inclusion of PROMs into routine clinical practice as a means of capturing aspects of health that are not easily assessed in a clinic visit.
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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 | 0.015 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".