Risk of surgical delivery to deep nuclei: A meta‐analysis
Bibliographic record
Abstract
Many novel strategies aimed at neuroprotection or neurorestoration involve surgical delivery of agents to deep nuclei along multiple trajectories. Using intracerebral hemorrhage on a per-trajectory basis as our primary end point, we quantified the level of surgical risk associated with agent delivery to deep nuclei. Secondarily, we quantified other event rates and examined relationships between intracerebral hemorrhage and 8 variables related to patient and practice characteristics. Meta-analytic techniques were used to pool complication rates reported in published articles involving deep brain stimulator electrode implantation or infusion of vectors, tissues, or trophic factors. One hundred nine studies were included in our analysis, comprising 6237 patients and 9890 trajectories to deep nuclei. The estimated per-trajectory intracerebral hemorrhage rate was 1.57% (95% confidence interval, 1.26%-1.95%). The proportion of trajectories leading to permanent or serious neurological deficits was 0.41% (0.28%-0.60%). The estimated mortality rate per trajectory was 0.14% (0.07%-0.29%). No relationship between intracerebral hemorrhage and sex, age, duration of disease, or exclusion of patients with surgical complications was observed; a significant positive relationship was observed with the use of microelectrode recording and a significant negative relationship with putamenal delivery. Our results show a significant difference in intracerebral hemorrhage rates between inoculations and electrode implantation. Our findings suggest that studies involving multiple trajectories to deep nuclei involve a high level of risk. However, inoculations may be significantly safer than electrode implantation. Our analysis has implications for the ethics of preclinical research, independent review of risk, subject selection, and adverse event reporting.
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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.042 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".