Geriatric Cerebrovascular Neurosurgery: Institutional Experience from Khoula Hospital
Bibliographic record
Abstract
Background: The aging of the healthy population without severe morbidity exposes them to cerebrovascular diseases and demand effective management. This study aimed to study the prevalence of geriatric cerebrovascular neurosurgical conditions in the Neurosurgical Department at Khoula Hospital, Muscat, Sultanate of Oman. Methods: A retrospective chart review of was done on geriatric cases admitted to the Neurosurgery Department in Khoula Hospital as an example of a neurosurgical center in a high-income developing country from January 2016 to 31st December 2019. Patients’ demographics, risk factors, diagnosis, Glasgow Coma Scale (GCS) on arrival, medications used, and length of stay were recorded. Results: 109 patients aged over 65 years were recruited in our retrospective review with a mean age of 74.12 years. Male-to-female ratio was (1.2:1). Intracerebral hemorrhage (ICH) was the most common vascular diagnosis (39.0%) followed by subarachnoid hemorrhage (SAH) (22.9%). Most patients (41.9%) had a GCS score of less than 8. About one-fifth of the patients received antiplatelet and anticoagulant medications. Most of the patients underwent surgical intervention (61.9%). 59% of the patients stayed in the hospital for less than 15 days. There were significant associations between the length of stay, treatment types (surgical vs. conservative), and age (P<0.05). Conclusion: Cerebrovascular pathologies are a growing cause of mortality and morbidity worldwide including developing countries because of the increasing number of elderly people. Antiplatelet medication and anticoagulants should be used with caution in the elderly.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".