Can We Predict Oral Antibiotic Treatment Failure in Children with Fast-Breathing Pneumonia Managed at the Community Level? A Prospective Cohort Study in Malawi
Bibliographic record
Abstract
BACKGROUND: Pneumonia is the leading cause of infectious death amongst children globally, with the highest burden in Africa. Early identification of children at risk of treatment failure in the community and prompt referral could lower mortality. A number of clinical markers have been independently associated with oral antibiotic failure in childhood pneumonia. This study aimed to develop a prognostic model for fast-breathing pneumonia treatment failure in sub-Saharan Africa. METHOD: We prospectively followed a cohort of children (2-59 months), diagnosed by community health workers with fast-breathing pneumonia using World Health Organisation (WHO) integrated community case management guidelines. Cases were followed at days 5 and 14 by study data collectors, who assessed a range of pre-determined clinical features for treatment outcome. We built the prognostic model using eight pre-defined parameters, using multivariable logistic regression, validated through bootstrapping. RESULTS: We assessed 1,542 cases of which 769 were included (32% ineligible; 19% defaulted). The treatment failure rate was 15% at day 5 and relapse was 4% at day 14. Concurrent malaria diagnosis (OR: 1.62; 95% CI: 1.06, 2.47) and moderate malnutrition (OR: 1.88; 95% CI: 1.09, 3.26) were associated with treatment failure. The model demonstrated poor calibration and discrimination (c-statistic: 0.56). CONCLUSION: This study suggests that it may be difficult to create a pragmatic community-level prognostic child pneumonia tool based solely on clinical markers and pulse oximetry in an HIV and malaria endemic setting. Further work is needed to identify more accurate and reliable referral algorithms that remain feasible for use by community health workers.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".