P.040 Value-based approach to the management of Inflammatory Neuropathies: Incorporating objective outcome measures in clinical care
Bibliographic record
Abstract
Background: Measuring outcomes that matter to patients is a key component of ensuring patient-centred care. In Chronic Inflammatory Neuropathies (CINs), where immunomodulatory treatments have risks and high costs, systematic evaluation of disease progression is needed to ensure patients are achieving outcomes that reflect their values and goals. The aim of this project is to evaluate the feasibility of objective outcome measure (OOM) use in the clinical setting. Methods: Prospective data was collected from 27 participants with CIDP or MMN. Participants completed and provided feedback on patient-reported outcome measures including quality of life, activity and participation, pain and fatigue, as well as grip strength, 9-hole peg test, 10 meter walk, muscle strength and sensation. Focus groups were conducted to collect qualitative data. Results: The majority of OOMs were considered relevant to 90% of participants. The top three ranked measures were muscle strength testing, daily activities questionnaire and quality of life questionnaire. 52% of participants identified balance and/or detailed gait assessment as an important factor that was not part of collected OOMs. Conclusions: OOMs allow for appropriate monitoring of patients and optimization of immunotherapy treatment. By tracking longitudinal results that matter to patients, patients can better participate in shared-decision making. Clinicians should adopt OOMs going forward.
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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 | 0.069 | 0.140 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".