Factors influencing the outcome of deep brain stimulation: Placebo, nocebo, lessebo, and lesion effects
Bibliographic record
Abstract
Deep brain stimulation (DBS) is a well-established treatment option for movement disorders, especially for Parkinson's disease (PD). There is a need to determine the role of expectation of benefit and the use of placebo to better understand the effects of electrode placement including the (micro)lesion effect. These factors must be understood to better interpret and attribute the therapeutic value of DBS. In this review, we critically present currently available data on the placebo, nocebo, lessebo, and lesion effects in the context of DBS. We provide a discussion of strategies that have the potential for controlling these effects in the setting of future DBS trials. We conclude that there is a need to standardize definitions for nocebo and (micro)lesion effects and that there are intrinsic limitations in defining the effect of expectation of benefit in DBS. These issues will be challenging to overcome especially with current technology and available study designs. New stimulation paradigms, better study designs, and the use of adaptive closed-loop DBS devices may facilitate a more accurate assessment of the placebo, nocebo, and lessebo effects in future DBS trials.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".