P.065 Understanding treatment barriers and adherence among people living with amyotrophic lateral sclerosis
Bibliographic record
Abstract
Background: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with only four approved treatments in North America - sodium phenylbutyrate (PB) and ursodoxicoltaurine (TURSO, also known as taurursodiol), riluzole, edaravone, and tofersen. Poor treatment adherence reduces clinical effectiveness which can adversely impact disease progression and mortality rates. Understanding barriers and adherence to treatment in clinical practice is essential to address these issues. Methods: A scoping review was conducted in PubMed, Medline, Embase, and Web of Science. Retained studies were, (1) published in English, (2) included adults with ALS, (3) explored treatment non-adherence and/or identified barriers associated with non-adherence in ALS in real world clinical practice, (4) focused on ≥1 of the four approved ALS medications, and (5) used a measurement of adherence. Observational studies, real-world data, and case reports were included. Quality assessment was performed. Results: The review illustrated several knowledge gaps, including limited data on the incidence of non-adherence to ALS treatment in clinical practice, a lack of understanding regarding barriers to treatment adherence in ALS, and an absence of studies outside of western societies. Conclusions: We demonstrate a dearth of real-world data on treatment adherence in ALS and highlight opportunities for advancing research into this important area.
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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 | 0.007 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".