Effects of Comorbidities on Lassa Fever: A 5-Year Retrospective Analysis of Cases Admitted in a Lassa Fever Research Institute in Nigeria (2019-2023)
Bibliographic record
Abstract
Background: Co-morbidities in Lassa fever refers to the presence of other underlying medical conditions or diseases in individuals infected with the virus. These co-morbidities can significantly affect the progression and outcome of Lassa fever, making it a complex and challenging infectious disease to control. Objective: To determine the effects of Co-morbidities on Lassa fever and it's management between 2019-2023 in a Lassa Fever Research Institute in Nigeria. Methodology: This study was conducted at the Lassa Fever Research Institute at Irrua Specialist Teaching Hospital (ISTH) in Edo State, Nigeria. It engaged a retrospective cross-sectional design and employed a systematic sampling technique. Data analysis was done using IBM SPSS version 21.0 software for descriptive statistics. Associations were tested using the Chi-square test, with a significance level set at p<0.05. Results: The study found that Lassa fever had no specificity for age as it affected individuals across a wide age range (18-78 years), with the highest incidence in the 47-57 year age group. Hypertension was the most common comorbid condition (30%), followed by peptic ulcer disease (20%). Ribavirin was the main stay of treatment used. The analysis showed no significant relationship between comorbidities and mortality, as the majority of cases (85%) had outstanding outcomes. However, there was a significant relationship (p=0.04) between the level of education and outcomes, with most individuals having a tertiary education and experiencing positive outcomes. Conclusion: According to this study, it was discovered that there were no significant relationship between co morbidities and mortality as majority of the cases reviewed were seen to have good outcomes with the aid of the current treatment (ribavirin).
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".