P.060 Provider and patient perspectives on outcome measure use in clinical care for chronic inflammatory neuropathy
Bibliographic record
Abstract
Background: The use of patient reported and functional outcome measures in routine practice enhances shared decision making and supports patient-centred care. This study compared the perspectives of Chronic Inflammatory Neuropathy (CIN) patients and providers regarding their experience using an outcome measure panel. Methods: A one year study was conducted to evaluate a nine measure outcome set in routine clinical practice for CIN. The panel included patient-reported outcome measures (e.g., I-RODS and EQ-5D-5L) and functional measures (e.g., grip strength). At the conclusion of the study, participants and providers completed an online questionnaire on their experience. Results: 25 patients and five providers completed the questionnaire. Both patients and providers reported benefit in tracking disease progression, supporting treatment-related decisions, and broadening views of health. Both groups agreed patient involvement in care was enhanced. Preference for specific measures, frequency, and data presentation differed. Providers emphasized integration into electronic medical records and streamlining processes. 100% of providers and 80% of patients wanted to continue completing outcome measures. Conclusions: CIN patients and providers recognize the value of integrating outcome measures into routine care. To effectively implement these measures in clinical settings, it is important to understand the patient and provider perspective and prevent unnecessary burdens to ensure sustainability of use.
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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 | 0.019 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".