Laboratory Monitoring Practices Among Canadian Multiple Sclerosis Clinicians
Bibliographic record
Abstract
BACKGROUND: Advances in multiple sclerosis (MS) disease modifying therapy (DMT) have increased laboratory monitoring requirements. Our goal was to survey existing practices and perceptions of risk in laboratory monitoring throughout Canada and assess whether opportunities to improve patient care and safety exist. METHODS: A web-based survey assessing prescriber demographics, current infrastructure, and concerns for lab monitoring was sent to the Canadian Network of MS Clinics (CNMSC) listserv, inviting MS clinicians across the country to participate. RESULTS: Respondents included 32/65 CNMSC-affiliated neurologists (49%), 6 registered nurses (RN), 2 nurse practitioners (NP), and 2 non-neurologist physicians from 8/10 provinces. For some questions, analysis was limited to 34 DMT-prescribing clinicians only. Despite broad implementation of electronic medical records (25/34, 74%), many prescribers (15/34, 44%) still receive laboratory results in paper form. In terms of lab monitoring infrastructure, we noted regional variability in the employment of nursing to monitor patient compliance with required laboratory monitoring. There is also a gap in laboratory surveillance, as less than 5% of respondents reported regularly reviewing results on weekends. Providers' length of practice and volume of MS patients were not associated with different perception of DMT laboratory monitoring risk. CONCLUSIONS: This nation-wide survey showed variability in infrastructure used in laboratory monitoring and regional variation in nursing involvement. Providers' level of concern for laboratory monitoring for DMTs did not vary by years of experience or volume of MS patients followed, suggesting that improved systems, rather than education, could ameliorate perceptions of risk.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".