In Reply: Quality of Life After Motor Cortex Stimulation: Clinical Results and Systematic Review of the Literature
Bibliographic record
Abstract
To the Editor: We thank Dr Alwardat and colleagues1 for the comments on our manuscript.2 As can be appraised in our review, measures of quality of life in the studies pooled for analysis were often conducted in an open label fashion. Only a few trials used statistical analyses to blindly quantify on/off stimulation changes. In this context, the majority of the literature provides class III evidence as far as quality of life measures are considered. Though only keywords were used, search terms were presented and the reasons for excluding articles were reported. General biases were noted in the discussion, along with the potential occurrence of a placebo effect. After starting the review, we were surprised to see that only 6 studies fulfilled our inclusion criteria and decided to analyze open label data from patients exiting a clinical trial currently being conducted in our institution. Our results show that MCS significantly improves visual analog pain scores and quality of life measures. Interestingly, no correlation was found between the 2, suggesting that changes in quality of life after motor cortex stimulation cannot be simply explained by the analgesic effects of this therapy. As it stands, our study provides class III data, corroborating previously published data. That said, it contrasts with previous reports, inasmuch as it was primarily focused on quality of life. This study was sponsored in part with funds from the Science without Border program of the Brazilian National Council for Scientific and Technological Development (CNPq; Conselho Nacional de Desenvolvimento Científico e Tecnológico; 400 201/2012-7). Devices were donated by St Jude Medical. Dr Hamani has received honoraria from St Jude Medical and Medtronic.
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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 | 0.006 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".