Intra-operative neurophysiological monitoring as an adjunct to resection of eloquent cerebral arteriovenous malformations: a retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Arteriovenous malformations (AVM) carry a risk of haemorrhage and may also cause epilepsy or ischaemic steal phenomenon. Surgical resection leads to high rates of lesion obliteration but resection of AVMs located in eloquent cortex is associated with high rates of morbidity. The aim of this study was to assess the outcomes following surgical resection of AVMs in eloquent cortical locations with the use of intra-operative neuro-monitoring (IoNM). METHODS: A prospectively maintained database of AVM resections between 2012 and 2023 was reviewed. Data describing demographic details, AVM characteristics and outcomes of patients who underwent surgical resection of AVMs located in eloquent areas with IoNM were extracted. Functional status was assessed using the modified Rankin scale (mRS). RESULTS: 191 patients underwent resection of an AVM, of which 10/191 (5%) underwent resection with IoNM. 7/10 patients were female and the median age was 40 years (range 25-57). 5/10 (50%) of the AVMs were ruptured. The AVM was completely resected in 10/10 (100%) of cases. New neurological deficits occurred in 6/10 (60%) with no permanent neurological deficits observed. The median period of follow-up was 12 months (range 2-46) and the functional status of every patient improved or remained stable compared to their preoperative status. CONCLUSION: IoNM may be a useful intra-operative adjunct during the resection of AVMs located within eloquent cortical areas. The use of IoNM should be considered when attempting surgical resection of AVMs located in eloquent cortical areas.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".