Ionic Disorders in Malaria and Dengue Co-Infection
Bibliographic record
Abstract
Introduction: The aim of this study was to investigate ionic disorders in malaria and dengue co-infection at Ouagadougou, Burkina Faso. Material and methods: This is a descriptive cross-sectional study with retrospective data collection, carried out in the laboratory of the Pediatric University Hospital Charles de Gaulle in Ouagadougou, Burkina Faso, from January 1st, 2017 to December 31st, 2019. The study was on patients who performed a thick blood drop/smear, dengue serology and blood ionogram. Results: On 1405 cases included in the study, 102 patients (7.26%) were confirmed of malaria. Dengue serology was positive in 235 patients (16.72%). The frequency of co-infection was 1.14% (n=16). The mean age of the patients was 9.93 years and the age group of 0 to 15 years represented 78.93% of the cases. There was a male predominance with a sex ratio (M/F) of 2.58. Hyponatremia (40%), hypocalcemia (40%), hypokalemia (30%) and hypophosphatemia (30%) were the main blood ionogram disturbances in malaria and dengue co-infection. The statistically significant disturbances in case of malaria and dengue co-infection were the absence of hypobicarbonatemia (p=0.036). Conclusion: Malaria and dengue are responsible for significant morbidity and mortality in Burkina Faso. Although co-infection was rare in the study (1.14%), it was associated with several blood ionogram disturbances. Evaluation and consideration of these disturbances during treatment would contribute to a better care of patients.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".