Metal allergy and neurovascular stenting: A systematic review
Bibliographic record
Abstract
BackgroundIntracranial stents and flow diverters contain significant amounts of metals, notably nickel, which can cause allergic reactions in a considerable portion of the population. These allergic responses may lead to complications like in-stent stenosis (ISS) and TIA/Stroke in patients receiving stents or flow diverters for intracranial aneurysms.MethodsWe conducted a systematic review of studies from inception until July 2023, which reported outcomes of patients with metal allergy undergoing neurovascular stenting. The skin patch test was used to group patients into those with positive, negative, or absent patch test results but with a known history of metal allergy.ResultsOur review included seven studies with a total of 39 patients. Among them, 87% had a history of metal allergy before treatment. Most aneurysms (89%) were in the anterior circulation and the rest (11%) were in the posterior circulation. Skin patch tests were performed in 59% of patients, with 24% showing positive results and 33% negative. Incidental ISS was observed in 18% of patients, and the rate of TIA/Stroke was reported in 21%. The pooled rates of ISS and TIA/Stroke were higher in the first group (43% and 38%) compared to the second (18% and 9%) and third groups (15% and 15%), but these differences were not statistically significant.ConclusionsThe current neurosurgical literature does not provide a conclusive association between metal allergy and increased complications among patients undergoing neurovascular stenting. Further studies are necessary to gain a more comprehensive understanding of this topic.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".