Therapeutic status quo in patients with relapsing-remitting multiple sclerosis: A sign of poor self-perception of their clinical status?
Bibliographic record
Abstract
BACKGROUND: Status quo (SQ) bias is defined as patient´s tendency to continue taking a previously selected but inferior therapeutic option. OBJECTIVE: To assess the presence of SQ bias and its associated factors in patients with relapsing-remitting multiple sclerosis (RRMS). METHODS: A multicenter, non-interventional study involving 211 patients with RRMS was conducted. Participants answered questions regarding risk preferences and management of simulated MS case-scenarios. The SymptoMScreen (SMSS) questionnaire was used to assess the perception of severity from the patients´ perspective. SQ bias was defined as patients' preference to maintain the current treatment despite evidence of disease activity. Mixed linear models adjusting for clustering assessed the association of candidate predictors with the outcome of interest. RESULTS: The mean age (SD) was 39.1 (9.5) years and 70.6% were women. SQ bias was observed in 74.4% (n=161) participants. Univariate analysis showed that SMSS score was associated with SQ bias (OR 1.04; 95% CI 1.01-1.07). Mixed linear regression models suggest that for every point increase in SMSS, there was a 4% increase in the likelihood of SQ bias (β 0.04; 95%CI 0.015-0.06; p<0.002). Among the different symptomatic dimensions included in the SMSS, only vision impairment (β 0.32; 95%CI 0.05-0.50) and depression (β 0.29; 95%CI 0.006-0.58) remained associated with SQ bias in the multivariate analysis. There was no association between participants' risk preferences and SQ bias. CONCLUSIONS: Unwillingness to pursue treatments that are more effective is a common phenomenon affecting over 7 out of 10 patients with RRMS. This phenomenon appears to be driven by patients' negative self-perception of their clinical status.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".