Utilization of Blood Culture in South Asia for the Diagnosis and Treatment of Febrile Illness
Bibliographic record
Abstract
Abstract Background Blood culture is the current standard for diagnosing bacteremic illnesses, yet it is not clear how physicians in many low- and middle-income countries utilize blood culture for diagnostic purposes and to inform treatment decisions. Methods We screened suspected enteric fever cases from 6 hospitals in Bangladesh, Nepal, and Pakistan, and enrolled patients if blood culture was prescribed by the treating physician. We used generalized additive regression models to analyze the probability of receiving blood culture by age, and linear regression models to analyze changes by month to the proportion of febrile cases prescribed a blood culture compared with the burden of febrile illness, stratified by hospital. We used logistic regression to analyze predictors for receiving antibiotics empirically. We descriptively reviewed changes in antibiotic therapy by susceptibility patterns and coverage, stratified by country. Results We screened 30 809 outpatients resulting in 1819 enteric fever cases; 1935 additional cases were enrolled from other hospital locations. Younger outpatients were less likely to receive a blood culture. The association between the number of febrile outpatients and the proportion prescribed blood culture varied by hospital. Antibiotics prescribed empirically were associated with severity and provisional diagnoses, but 31% (1147/3754) of enteric fever cases were not covered by initial therapy; this was highest in Pakistan (50%) as many isolates were resistant to cephalosporins, which were commonly prescribed empirically. Conclusions Understanding hospital-level communication between laboratories and physicians may improve patient care and timeliness of appropriate antibiotics, which is important considering the rise of antimicrobial resistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".