Geriatric Neuro-Oncology in the Middle East: A Sultanate of Oman Experience
Bibliographic record
Abstract
Brain tumors are primary or metastatic malignancies of the central nervous system (CNS) with significant morbidity and mortality. The overall prevalence of cancer including brain cancer has increased by more than 10% according to the National Institute of cancer statistics. The average percent increase in primary brain tumor incidence for ages 75–79, 80–84, and 85 and older were 7%, 20.4%, and 23.4%, respectively. This manuscript describes a retrospective study of geriatric cases admitted to the Neurosurgical Department in Khoula Hospital (KH) and diagnosed with brain cancer from 1 January 2016 to 31 December 2019. Of the study cohort, 58.5% were more than 75 years of age. The male-to-female ratio was (1:1.1). Meningiomas are found to be the commonest tumor (52.8%) followed by glioblastoma (GBM) (18.9%). Most of the patients had a Glasgow coma scale (GCS) score of 14–15 on admission (69.9%). Patients diagnosed with a non-meningioma tumor had lower GCS score on admission compared to meningioma patients with statistical significance (p = 0.04). Also, there was a significant difference between the length of stay (LOS) and the type of intervention (surgical vs. conservative), in which patients received a conservative type of management found to have a shorter length of stay (LOS) compared with the patients who underwent surgical intervention (p < 0.005). In Oman, the number of geriatric oncology cases remained stable over the 4 years. The incidence of geriatric neuro-oncology cases was higher in patients aged more than 75 years of age. Finally, the GCS score was affected by the type of tumor. The length of stay varies according to the treatment administered. Special care must be taken when dealing with geriatric neuro-oncological cases due to the high potential rate of mortality and morbidity among those group, and a more holistic approach is recommended as an essential need to evaluate the overall situation of those patients and manage them accordingly.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".