Utilization of anticoagulant and antiplatelet medications among geriatric patients with neurosurgical diseases
Bibliographic record
Abstract
Aim: To study the utilization patterns of anticoagulant and antiplatelet medications among geriatric patients with neurosurgical conditions in the neurosurgical department at Khoula Hospital, Muscat, Sultanate of Oman. Introduction: Anticoagulant and antiplatelet presumption is a worry in neurosurgical patients, that suggests a subtle balance between the risk of thromboembolism against the risk of peri- and postoperative haemorrhage. Patients taking those medications were found to have an increased risk of bleeding from traumatic and traumatic events. Materials and Methods: A retrospective study of geriatric cases admitted to the Neurosurgery Department in Khoula Hospital (KH) as an example of a neurosurgical center in Sultanate of Oman, from January 2016 to 31st December 2019. Patients demographics, diagnosis, length of hospital stay (LOS), Glasgow Coma Scale (GCS), length of ICU admission, and treatment proposed were recorded. Results: The most common diagnostic category was trauma (35.4%). 16.0 % of the patients were taking anticoagulant medications. Patients with traumatic brain injury (TBI) were found to have a higher rate of using anticoagulant medications (36.6%). There was a significant difference between the LOS, type of intervention, ICU admission, and the usage of anticoagulant and antiplatelet drugs (p<0.05). Enoxaparin was the most commonly used anticoagulant agent. 19.6 % of the patients were taking antiplatelet medications. This study was showed that aspirin is the most commonly used antiplatelet agent among different neurosurgical pathologies. Conclusion: Patients with TBI were found to have a higher rate of using anticoagulant medications. Decisions regarding prescription and resumption of anticoagulants and antiplatelet medications should be taken on a case-by-case basis involves multidisciplinary and holistic approaches.
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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 | 0.000 | 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".