Hematological, cholesterolemic and clinical profile in exposed to outdoor air pollution linked to the incineration of biomedical waste in Benin
Bibliographic record
Abstract
Introduction: Incineration is one of the treatment methods for biomedical waste (BMW). It is a source of toxic air pollutant emission. The study aimed to compare the haematological, cholesterolemic and clinical profile of subjects exposed to this pollution to controls. Methods: This was a comparative cross-sectional study on 154 exposed subjects selected within 500 meters of the incineration sites of solid biomedical waste and controls, among new blood donors, matched on age, sex, level of education, cooking mode. The hematological parameters and cholesterolemia were obtained by standardized procedure on automata. The proportions of subjects were compared with a chi-square test. The dispersion analysis of the biological parameters was made by the non-parametric Wilcoxon test at the 5% threshold. Results: The two groups were comparable for matching. The frequency of respiratory symptoms ranged from 20.1 - 79.2% in exposed versus 4.5 - 54.5% in controls. The frequency of neurological symptoms ranged from 26.6 - 76.6% in exposed versus 1.9 - 51.9% in controls. Hemoglobin (p=0.001), hematocrit (p=0.051) were low in the exposed and white blood cells (p=0.003) and platelets (p<0.001) high. Total cholesterol (p<0.001) was twice as high and HDL six times lower in the exposed (p<0.001). Conclusion: The exposed presented a more altered hematological and cholesterolemic profile with more frequent symptoms. It is essential to improve the management practice of BMW in our hospitals through the use of innovative, less polluting technologies
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".