Pattern and Outcome of Paediatric Non-Communicable Diseases in a Teaching Hospital in Southern Nigeria
Bibliographic record
Abstract
Non-communicable diseases (NCDs) are chronic non-transmissible diseases that are mainly attributable to lifestyle changes. There is a global increase in this category of diseases, which in developing countries constitute an added burden to the already existing burden of communicable diseases. This study aims at determining the prevalence, pattern, length of hospital stay and outcome of children admitted with non-communicable diseases. Methods: This is a retrospective cross-sectional study carried out in the paediatric wards of our hospital. Data was extracted from records of children admitted within the study period. The data was analyzed using the Statistical Package for Social Sciences (IBM SPSS) version 23. The student t-test was used to compare the means between two groups, while an ANOVA was used for more than two groups. Result: Out of 820 children studied, 32.2% had NCDs with sickle cell disease, neoplasms, and cardiovascular and neurological diseases, constituting the major non-communicable diseases recorded. There was a significantly longer duration of hospital stay and a higher mortality rate in patients admitted with an NCD. There was a significant association between mortality and the category of NCD, with a greater contribution from neoplastic diseases. Conclusion: There is a high prevalence of paediatric non-communicable diseases, although communicable diseases are still more prevalent among children in this study. This is associated with poor outcomes and a longer duration of hospital stay.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".