Third Nerve Palsy Due to Intracranial Aneurysms and Recovery after Endovascular Coiling
Bibliographic record
Abstract
INTRODUCTION: The modality of treatment of third nerve palsy (TNP) associated with intracranial aneurysms remains controversial. While treatment varies with the location of the aneurysm, microsurgical clipping of PComm aneurysms has generally been the traditional choice, with endovascular coiling emerging as a reasonable alternative. METHODS: Patients with TNP due to an intracranial aneurysm who subsequently underwent treatment at a mid-sized Canadian neurosurgical center over a 15-year period (2003-2018) were examined. RESULTS: A total of 616 intracranial aneurysms in 538 patients were treated; the majority underwent endovascular coiling with only 24 patients treated with surgical clipping. Only 37 patients (6.9%) presented with either a partial or complete TNP and underwent endovascular embolization; of these, 17 presented with a SAH secondary to intracranial aneurysm rupture. Aneurysms associated with TNP included PComm (64.9%), terminal ICA (29.7%), proximal MCA (2.7%), and basilar tip (2.7%) aneurysms. In general, smaller aneurysms and earlier treatment were provided for patients for ruptured aneurysms with a shorter mean interval to TNP recovery. In the endovascularly treated cohort initially presenting with TNP, seven presented with a complete TNP and the remaining were partial TNPs. TNP resolved completely in 20 patients (55.1%) and partially in 10 patients (27.0%). Neither time to coiling nor SAH at presentation were significantly associated with the recovery status of TNP. CONCLUSION: Endovascular coil embolization is a viable treatment modality for patients presenting with an associated cranial nerve palsy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".