Bibliographic record
Abstract
Edgar Durand,1 Alexander G Weil,2 Marc Lévêque1,3 1Espace éthique de l’Assistance publique-Hôpitaux de Paris et Département de recherche en éthique, South Paris University, Paris, France; 2Pediatric Neurosurgical Department, Sainte-Justine Hospital, Montreal, QC, Canada; 3Service de Neurochirurgie, Hôpital de la Pitié-Salpêtrière, Paris, France We read with interest the article entitled “Anterior capsulotomy improves persistent developmental stuttering with a psychiatric disorder: a case report and literature review” published in Neuropsychiatric Disease and Treatment.1 The authors report on a 28-year-old man with persistent developmental stuttering who was treated by bilateral anterior capsulotomy in the People’s Republic of China. To our knowledge, this is a new and previously unreported application of this technique for this indication. Accordingly, as the authors highlight, “the evidence for surgical treatment of persistent developmental stuttering and associated psychiatric disorders is limited”, placing their approach within the clinical research forum. In this experimental context, this case report brings forward several important reflections on patient evaluation, technique utilized, and postoperative follow-up. Read the original article
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".