On how patients with multiple sclerosis weigh side effect severity and treatment efficacy when making treatment decisions.
Bibliographic record
Abstract
Although effective disease-modifying treatments (DMTs) are available for individuals suffering from multiple sclerosis (MS), many patients fail to take their recommended medications. Unlike medications that provide immediate relief from existing symptoms, DMTs decrease the probability of future symptoms (i.e., a probabilistic benefit) while concurrently carrying an appreciable risk of immediate side effects (i.e., a probabilistic cost). Prior research has shown that both the probability of reducing disease progression and the probability of experiencing side effects impact patients' likelihood of taking a hypothetical DMT. The role that side effect severity plays in treatment decisions remains unexplored. The present study examined how probability of medication efficacy and side effect severity impact patients' likelihood of taking hypothetical DMTs. Patients' likelihood of taking a DMT systematically decreased as medication efficacy decreased and side effect severity increased. Because side effect severity appears to impact decision-making processes in unique ways, the present results suggest that providers should present information on severe (which are typically rare) and mild to moderate side effects (which are more common) separately. (PsycINFO Database Record
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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 | 0.046 | 0.335 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".