Can we distinguish pneumonia from wheezy diseases in tachypnoeic children under low-resource conditions? A prospective observational study in four Indian hospitals
Bibliographic record
Abstract
BACKGROUND: Acute respiratory infections are the commonest cause of mortality and morbidity in children worldwide. A quarter of all deaths occur in India alone. In order to reduce this disease burden, there is a need for better diagnostic criteria, particularly ones allowing early detection of high-risk children. METHODS: We enrolled 516 under 5 year olds, in four Indian hospitals, who met WHO age-dependent tachypnoea criteria for pneumonia at presentation. Patients underwent a protocolised examination assessing 29 items, including history, examination, O2 saturation, plus scores for chest X-ray, auscultation and conscious level. Treatment was determined by the emergency room (ER) physician. All children were reviewed at day 4 by a paediatrician and placed into four diagnostic categories: pneumonia, wheezy disease, mixed and non-respiratory. RESULTS: The majority had wheezy diseases (42.8%). The remainder had pneumonia (35.9%), mixed disease (18.6%) and non-respiratory (2.7%). Best diagnostic predictors for wheezy disease were (auscultation/previous similar episodes) and for pneumonia (auscultation/CXR score). Mortality was 1.6%. Best disease severity predictors were conscious level, weight/age z score and respiratory/pulse rates. INTERPRETATION: Current tachypnoea-based algorithms significantly overdiagnose pneumonia in children and underdiagnose wheezy diseases. Diagnostic accuracy can be improved by various combinations of clinical variables, but the best single diagnostic predictor is auscultation. Simple criteria can also be defined that reliably detect which tachypnoeic children are at high risk of death or deterioration. Management plans based on these protocols could reduce unnecessary antibiotic use, improve the management of wheezy diseases and reduce mortality by earlier identification of high-risk children.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".