Epidemiology of patients presenting to the emergency centre of Princess Marina Hospital in Gaborone, Botswana
Bibliographic record
Abstract
Introduction Emergency medicine is a newly recognized specialty in Botswana and the country launched an emergency medicine residency in January 2011. Data regarding the practice of emergency medicine in Botswana are limited. This study reviewed 1 year of patient presentations to the emergency centre of Princess Marina Hospital, the country’s main referral hospital located in the capital city, Gaborone. Methods Epidemiologic data of all patients presenting to the emergency centre between May 2010 and April 2011 were extracted into a database. The diagnoses of a random sample of patient presentations were coded using the categories outlined by the Clinical Classifications Software (CCS) for ICD-10. For ease of analysis, several CCS categories were grouped together for subsequent analysis. Results 24,905 patient encounters were recorded during the study period. A large proportion of patients were aged between 25 and 50 years old. 20% of patients presented with a traumatic injury. The most common diagnoses across all ages included trauma, pregnancy complications, gastrointestinal disorders, and pneumonia. Conclusion These results can inform the development of emergency medicine education and acute care systems in Botswana.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".