P.029 Assessing disability in MS during the COVID-19 pandemic: correlation between PDDS and EDSS scores obtained before and after virtual assessments
Bibliographic record
Abstract
Background: Public health measures during the COVID-19 pandemic resulted in many multiple sclerosis (MS) patients being assessed virtually. Expanded Disability Status Scale (EDSS) scores, which are routinely obtained during MS consults, cannot be reliably calculated during virtual assessments. The Patient Determined Disease Steps (PDDS) is a validated patient-reported outcome measure of disability in MS. This study aimed to find real world evidence for the validity of PDDS as a surrogate of EDSS. Methods: Chart review of all MS patients from the MS Clinic in Saskatoon, Saskatchewan who completed PDDS forms emailed to them prior to their virtual visit (N = 277) was performed. 97 (35%) had documented EDSS scores prior to and following their self-reported PDDS. Correlational analysis between PDDS scores and pre and post EDSS scores was performed. Results: PDDS scores were highly correlated with EDSS scores before (r(95) = .79, p < .001) and after (r(95) = .84, p < .001) clinic closure occurred. Conclusions: This study provides real-world evidence that PDDS can accurately assess disability in MS when in-person assessments are not possible. Further investigation into patient demographics that increase the likelihood of completing PDDS assessments prior to appointments at our centre is ongoing.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".