Navigating the “liberation procedure”: a qualitative study of motivating and hesitating factors among people with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: The debate within the multiple sclerosis (MS) community initiated by the chronic cerebrospinal venous insufficiency (CCSVI) hypothesis and the subsequent liberation procedure placed some people with MS at odds with health care professionals and researchers. OBJECTIVE: This study explored decision making regarding the controversial liberation procedure among people with MS. SUBJECTS AND METHODS: Fifteen people with MS (procedure, n=7; no procedure, n=8) participated in audiotaped semistructured interviews exploring their thoughts and experiences related to the liberation procedure. Data were transcribed and analyzed using an iterative, consensus-based, thematic content-analysis approach. RESULTS: Participants described an imbalance of motivating factors affirming the procedure compared to hesitating factors that provoked the participant to pause or reconsider when deciding to undergo the procedure. Collegial conversational relationships with trusted sources (eg, MS nurse, neurologist) and ability to critically analyze the CCSVI hypothesis were key hesitating factors. Fundraising, family enthusiasm, and the ease of navigation provided by medical tourism companies helped eliminate barriers to the procedure. CONCLUSION: Knowledge of factors that helped to popularize the liberation procedure in Canada may inform shared decision making concerning this and future controversies in MS.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".