Geriatric Neurosurgery in High-Income Developing Countries: A Sultanate of Oman Experience
Bibliographic record
Abstract
This study aimed to investigate the prevalence and characteristics of geriatric neurosurgical conditions in the Neurosurgical Department at Khoula Hospital (KH), Muscat, Sultanate of Oman. The majority of various neurosurgical conditions is increasing in elderly patients, which leads to an increase in neurosurgical demand. The aging population has a direct effect on hospital decision-making in neurosurgery. However, limited data are available to assess geriatric neurosurgery in developing countries. A retrospective chart review of geriatric cases admitted to the Neurosurgery Department in KH served as our 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 on arrival, treatment types, and length of stay were recorded. A total of 669 patients who were above the age of 65 years were recruited into our retrospective review. The mean age was 73.34 years in the overall cohort and the male-to-female ratio was (1.6:1). The most common diagnostic category was trauma, which accounted for 35.4% followed by oncology and vascular (16.3% each). Hydrocephalus accounted for 3.7% of the admissions. Most of the patients underwent surgical interventions (73.1%). The associations were significant between the treatment types (surgical vs. conservative), Length of Stay, and the GCS on arrival (p < 0.05). In conclusion, the trend of geriatric neurosurgery is increasing in developing countries. The most common reason for admission to the neurosurgical ward was Traumatic Brain Injury. Special care must be taken when dealing with geriatric neurosurgical cases and a more holistic approach is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".