Caught in a no-win situation: discussions about CCSVI between persons with multiple sclerosis and their neurologists – a qualitative study
Bibliographic record
Abstract
BACKGROUND: In recent years, shared decision making (SDM) has been promoted as a model to guide interactions between persons with MS and their neurologists to reach mutually satisfying decisions about disease management - generally about deciding treatment courses of prevailing disease modifying therapies. In 2009, Dr. Paolo Zamboni introduced the world to his hypothesis of Chronic Cerebrospinal Venous Insufficiency (CCSVI) as a cause of MS and proposed venous angioplasty ('liberation therapy') as a potential therapy. This study explores the discussions that took place between persons with MS (PwMS) and their neurologists about CCSVI against the backdrop of the recent calls for the use of SDM to guide clinical conversations. METHODS: In 2012, study researchers conducted focus groups with PwMS (n = 69) in Winnipeg, Canada. Interviews with key informants were also carried out with 15 participants across Canada who were stakeholders in the MS community: advocacy organizations, MS clinicians (i.e. neurologists, nurses), clinical researchers, and government health policy makers. RESULTS: PwMS reported a variety of experiences when attempting to discuss CCSVI with their neurologist. Some found that there was little effort to engage in desired discussions or were dissatisfied with critical or cautious stances of their neurologist. This led to communication breakdowns, broken relationships, and decisions to autonomously access alternative opinions or liberation therapy. Other participants were appreciative when clinicians engaged them in discussions and were more receptive to more critical appraisals of the evidence. Key informants reported that they too had heard of neurologists who refused to discuss CCSVI with patients and that neurology as a whole had been particularly vilified for their response to the hypothesis. Clinicians indicated that they had shared information as best they could but recommended against seeking liberation therapy. They noted that being respectful of patient emotions, values, and hope were also key to maintaining good relationships. CONCLUSIONS: While CCSVI proved a challenging context to carry out patient-physician discussions and brought numerous tensions to the surface, following the approach of SDM can minimize the potential for unfortunate outcomes as much as possible because it is based on principles of respect and more two-way communication.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".