Prevalence and Outcomes of Geriatric Traumatic Brain Injury in Developing Countries: A Retrospective Study
Bibliographic record
Abstract
Abstract Introduction The world populace is aging and it is anticipated that 2 billion people will be older than 60 years by 2050. Traumatic brain injury (TBI) is a major cause of death and disability worldwide. In the United States, 2.8 million people pursue medical attention yearly. TBI exemplifies a leading cause of mortality and morbidity among the geriatric age group worldwide. Methods A retrospective study of geriatric cases who were admitted to the Neurosurgery Department in Khoula Hospital from January 1, 2016, to December 31, 2019, was conducted. Patients' demographics, risk factors, neuro-vital sign, diagnosis, Glasgow coma scale (GCS) on arrival, treatment types, and length of stay (LOS) were recorded. Results Two hundred and thirty-four patients were admitted due to TBI in four years period. Seventy-five percent of the study cohort were more than 75 years old. Male to female ratio was 2.4:1. Subdural hematoma (SDH) was the most common TBI diagnosis based on computed tomography (77.4%). Most of the patients were having GCS scores of 14 to 15 (67.9%). Sixteen percent of the patients received antiepileptic medications. The majority of the patients underwent surgical intervention (70.5%). Eighty percent of the patients stayed in the hospital for less than 15 days. There was a significant difference between the LOS and type of surgery. Subarachnoid hemorrhage was found to have the highest mean age (79.7 years). Intracerebral hemorrhage patients had the longest LOS in the hospital with a mean of 44.2 days. There was no significant difference between the age of patients and type of surgery. Conclusion The number of TBI in the elderly population is increasing annually. The most common type of TBI in our cohort was SDH and most of the patients were treated with burr hole surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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".