Nonmedical Switching From Originators to Biosimilars: Does the Nocebo Effect Explain Treatment Failures and Adverse Events in Rheumatology and Gastroenterology?
Bibliographic record
Abstract
The act of nonmedical switching, defined as switching stable patients who are generally doing well with their current therapy from an originator biologic to its biosimilar, has been endorsed as a reasonable treatment strategy. The safety and efficacy of nonmedical switching have been evaluated in randomized controlled and real-world evidence studies, which have demonstrated that although many patients maintain treatment response after the switch, some patients experience therapy failure, resulting in therapy discontinuation. It has been postulated that the vast majority, if not all, of these treatment failures result from a "nocebo effect", defined as patients' negative expectations toward the therapy change. Reports suggest that the risk of a nocebo effect is higher following a mandated nonmedical switch. Although the nocebo effect is a well-recognized phenomenon in pain studies, evidence is limited in immune-mediated diseases primarily because it is difficult to quantify, especially retrospectively. In spite of this, numerous biosimilar studies in patients with immune-mediated diseases have concluded that nonmedical switching failures are due to a nocebo effect. The objective of this narrative review was to explore the reasons for nonmedical switch failure or discontinuation and the role of the nocebo effect among patients with inflammatory rheumatic and gastrointestinal diseases who switched from an originator biologic to its biosimilar.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".