Potentially Avoidable Hospitalizations at Grand Yoff General Hospital, Senegal
Bibliographic record
Abstract
BACKGROUND: PAHs are necessary hospitalizations that could be prevented by appropriate primary care. They are mainly attributable to non-communicable diseases (NCDs), which are increasing, especially in developing countries. The objective of this research was to evaluate the epidemiological burden of the PAHs at Grand Yoff General Hospital.METHODOLOGY: A cross-sectional study was carried out in 2015. The population consisted of all patients hospitalized from April to August 2015, except those who were admitted to surgery, maternity or neonatology departments. This was a comprehensive study; the cases were represented by patients admitted for diabetes, high blood pressure, pulmonary disease, chronic kidney disease or stroke.RESULTS: A total of 739 hospitalizations were recorded in the targeted services, including 110 cases of PAHs (14.88%). Pulmonary disease was slightly more frequent (4.74%), followed by diabetes (4.08%), stroke (3.65%), then chronic kidney disease (1.35%) and high blood pressure (1.08%). The average age of cases was 57 ±17.49 years and 54.5% of patients were 60 years of age or older, the sex ratio was 0.96, the married 68.2%, and the widowed 20%. About 34% of the patients were uneducated and 24% had just a primary school level. Only 8.2% were employed, while 43% were housewives and 23% were retired. 70% got a monthly income less than 100 USD. The median length of stay was 5 days. The level of awareness of the severity of the disease had improved significantly, from 37.3% at entry to 71.8% at the end of the stay (p <0.01). That was the same for the level of information about the means of preventing disease prevention, from 32.7% to 64.5% with a p value <0.01.A similar frequency of PAHs was reported in another study carried out in a regional hospital in northern Senegal (15%) with a predominance of the elderly.CONCLUSION: PAHs are a heavy burden at Grand Yoff General Hospital. Strengthening primary health care through promotion and prevention is an alternative, especially for poor populations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".