Antibiotic use and resistance in children with severe acute malnutrition and human immunodeficiency virus infection
Bibliographic record
Abstract
Severe acute malnutrition (SAM) and human immunodeficiency virus (HIV) infection underlie a major proportion of the childhood disease burden in low- and middle-income countries. These diseases commonly co-occur and lead to higher risk of other endemic infectious diseases, thereby compounding the risk of mortality and morbidity. The widespread use of antibiotics as treatment and prophylaxis in childhood SAM and HIV infections, respectively, has reduced mortality and morbidity but canlead to increasing antibiotic resistance. Development of antibiotic resistance could render future infections untreatable. This review summarises the endemic co-occurrence of undernutrition, particularly SAM, and HIV in children, and current treatment practices, specifically WHO-recommended antibiotic usage. The risks and benefits of antibiotic treatment, prophylaxis and resistance are reviewed in the context of patients with SAM and HIV and associated sub-populations. Finally, the review highlights possible research areas and populations where antibiotic resistance progression can be studied to best address concerns associated with the future impact of resistance. Current antibiotic usage is lifesaving in complicated SAM and HIV-infected populations; nevertheless, increasing baseline resistance and infection remain a significant concern. In conclusion, antibiotic usage currently addresses the immediate needs of children in SAM and HIV endemic regions; however, it is prudent to evaluate the impact of antibiotic use on resistance dynamics and long-term child health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".