Research-to-Practice Gaps in Multiple Sclerosis Care for Patients with Subjective Cognitive, Mental Health, and Psychosocial Concerns in a Canadian Center
Bibliographic record
Abstract
BACKGROUND: People with multiple sclerosis (MS) are at increased risk for cognitive impairment, mental health concerns, and psychosocial issues, which can negatively affect disease outcomes and quality of life. Current MS care guidelines recommend integrated interdisciplinary services to address these concerns; however, issues can be overlooked during routine care. To date, there is inadequate research on how often these issues are identified and addressed during routine MS care. METHODS: One hundred medical records were randomly selected and reviewed (55 relapsing-remitting MS, 17 secondary progressive MS, 8 primary progressive MS, and 20 other or subtype not indicated). All visits to, and contacts with (ie, telephone, e-mail), an MS clinic over 1 year were included in the analysis to determine the proportion of patients presenting with cognitive, mental health, and psychosocial concerns and the proportion of patients offered associated services. RESULTS: Of the 25 patients with at least one identified concern, treatment recommendations occurred for 13 (52%). Rates of identification of cognitive, mental health, and psychosocial concerns in standard clinical practice were significantly lower than the identified prevalence in epidemiologic studies. Demographic factors had no bearing on who was offered treatment. Patients with concerns access MS clinic services more often than those without. CONCLUSIONS: Discrepancies between reported and expected frequencies may be due to overreliance on patient self-disclosure and concerns by the health care team that inadequate resources are available to address issues. An interdisciplinary team model may help address these issues.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".