Drowning in the great lakes of Uganda: a neglected problem
Bibliographic record
Abstract
Background Drowning has continued to be one of the leading causes of injury deaths in the great lakes of Uganda. Activities of boat transportation and commercial fishing have contributed to drowning. There are neither community studies nor mandatory death registrations to drowning related data. Aim To compare drowning and near drowning cases reported in the hospital trauma registries and the print media in the year 2010 in Uganda. Methods Print media reports in four English newspaper magazines in Uganda (one government owned and three private owned), published between January and July 2010 were reviewed and compared these with reported drowning and near drowning cases recorded in a nationwide regional hospital based trauma registry. Results 98 persons were reported in four newspapers. 61(62.2%) drowning deaths were confirmed while a total of 21(21.4%) were near drowning cases. There was inconclusive information regarding the survival and or rescue of the remaining 16 (16.3%) persons. 2834 trauma registry cases in the same period of 2010, indicated three (0.1%) cases were recorded. All these were near drowning cases. Significance There is limited information on the burden of drowning as a result of no systematic process to record and track drowning related incidents and deaths. Hospital trauma registries under estimate the drowning cases thus systematic community based surveys are needed to quantify the burden of drowning and act as a baseline research for drowning interventional research in Uganda.
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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 | 0.001 | 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".