Virtual multidisciplinary ALS clinic care during the COVID-19 pandemic: clinical outcomes in a Canadian cohort (Preprint)
Bibliographic record
Abstract
BACKGROUND The COVID-19 pandemic brought significant challenges to ALS care, which is recommended to include provision within specialized multidisciplinary clinics, and further highlighted the potential for virtual care (VC) strategies. There is very limited data pertaining to clinical outcomes associated with VC in ALS. OBJECTIVE In the ALS Clinic context, we aimed to assess the impact of VC on patient-reported quality of life (QOL), clinical progression, caregiver burden, and travel distance/time savings. METHODS Eleven participants with ALS, receiving synchronous virtual multidisciplinary ALS Clinic care, had baseline and 6-month follow-up outcome data compared across the EQ-5D-5L, ALS Functional Rating Scale-Revised (ALSFRS-R), Clinical Frailty Scale (CFS), and Zarit Burden Interview (ZBI). VC was provided by video or audio-based platforms. RESULTS For QOL via EQ-5D-5L, improvement was noted on several dimensions, including Anxiety/Depression, while the Paretian Classification of Health Change identified general stability. Mean (SD) EQ-VAS patient percieved health scores trended towards improvement [51.3 (22.3) to 57.4 (21.6) (P=.591)]. Mean ALSFRS-R scores declined 0.62 points per month, while mean (SD) CFS scores increased [5 (1.48) to 5.8 (1.14)]. Mean (SD) ZBI caregiver burden scores trended down [24.67 (17.61) to 20.83 (16.68) (P=.334)], as did the proportion with high burden [55.5% to 33.3% (P=.400)]. Mean travel distance/time saved per patient per visit was 167.8 km/1 hour 54 minutes. CONCLUSIONS We demonstrate successful application of synchronous virtual multidisciplinary ALS Clinic care, noting improvement in several patient-reported QOL domains, trends towards reduced caregiver burden, and considerable reduction in travel time over an interval with expected clinical progression.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".