Injury in Kampala, Uganda: 6 years later.
Bibliographic record
Abstract
BACKGROUND: Trauma remains a tremendous cause of morbidity and mortality in most countries. The objective of our study was to describe injury from trauma at the major referral hospital in Uganda over a 1-year period. METHODS: Trauma registry forms have been completed for all trauma patients seen between August 2004 and July 2005 at the casualty department of Mulago Hospital in Kampala, Uganda. We also obtained 2-week follow-up data, and we compared these data with 1998 data from the same institution. RESULTS: In all, 3778 patients were entered into the database, with complete data available for 93.5% of patients. Patients had a mean age of 26 (standard deviation [SD] 12) years, and 75% of patients were male. The mean Kampala Trauma Score (KTS) was 9.1 (SD 1). We classified injuries as mild (82%; KTS 9-10), moderate (14%; KTS 7-8) and severe (4%; KTS <or= 6). On arrival, 57% of patients were treated and sent home, 41.6% were admitted and 0.4% died in the casualty department. At 2-week follow-up, 85% were discharged, 12% were still in hospital and 2.7% had died. Causes of injury included road traffic collisions (50%), blunt force (15%), falls (10%), stab wounds (9%), animal bites (7%), burns (6%) and gunshot wounds (1%). Causes of mortality were road traffic collisions (61%), burns (15%), blunt trauma (8.6%), falls (6.5%), stabs/cuts (5.4%) and other (3.3%). Data from 1998 demonstrated a similar spectrum of injuries but with a mortality of 7.2%. CONCLUSION: Road traffic collisions are the greatest cause of morbidity and mortality from injury in Kampala, Uganda. When comparing data from 1998 and 2005, the spectrum of injury remained similar, but mortality decreased from 7.2% to 2.7%.
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 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".