Simultaneous intrastriatal and intranigral fetal dopaminergic grafts in patients with Parkinson disease: a pilot study
Bibliographic record
Abstract
The main neural transplantation strategy in Parkinson disease (PD) has been focused on reinnervating the striatum. The clinical results reported in patients who receive transplants have been limited and do not justify the use of neural transplantation as a routine therapeutic procedure for PD. Identifying the optimal target for transplantation may be one of the critical factors for optimizing clinical outcomes. Evidence from preclinical studies indicates that simultaneous intrastriatal and intranigral grafts (double grafts) may produce a more complete functional recovery. The authors report the clinical and positron emission tomography (PET) scanning results in three patients enrolled in a safety and feasibility pilot study who received double grafts and who have been followed for up to 13 months posttransplantation. Patients included in the study had idiopathic PD. All patients underwent detailed assessments before and after surgery, in accordance with the Core Assessment Program for Intracerebral Transplantation. The patients received implants of fetal mesencephalic cell suspensions in the putamen and substantia nigra (SN) bilaterally. There were no intraoperative or perioperative complications. Follow-up PET scans demonstrated an increase in the mean fluorodopa uptake constant values in the putamen and SN 12 months postsurgery. Improvements were also noted in the total Unified Parkinson's Disease Rating Scale, Hoehn and Yahr, Schwab and England, and pronation/supination scores after transplantation. The authors demonstrate the feasibility of reinnervating the SN and striatum by using a double transplant strategy in humans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".