Feasibility of manual white blood cell counts as a predictor of neonatal sepsis in a low-resource setting
Bibliographic record
Abstract
BACKGROUND: Manual white blood cell (WBC) differential counts as a predictor for neonatal sepsis development in a low-resource setting have not been thoroughly evaluated. We hypothesized that manual differentiation (specifically immature:total [I:T] neutrophil ratios) would be feasible and useful as an adjunct to predict early-onset neonatal sepsis (EONS). Secondarily, we hypothesized that vaccination with bacillus Calmette-Guérin (BCG) and oral polio vaccine (OPV) could alter WBC differential counts and thus might reduce its predictive performance. METHODS: We performed a prospective cohort study within a randomized trial, randomizing healthy, high-risk newborns admitted to the nursery at the national hospital in Guinea-Bissau 1:1 to BCG+OPV at admission or at discharge (usual practice). Thin capillary blood films were prepared at 2 d of age in a subset of 268 neonates. WBC counts were assessed by microscopy and neonates were followed up for sepsis development within 2 weeks. RESULTS: Ninety-eight percent (264/268) of smears provided interpretable reads. Of the 264 children, 136 had been randomized to receive BCG+OPV prior to sampling; the remaining 128 were vaccinated at discharge. The I:T ratio (average 0.017) was lower among children who did not develop clinical sepsis but did not predict sepsis (p=0.70). Only three children had an I:T ratio >0.2 (associated with a higher probability of clinical sepsis in previous studies) but did not develop sepsis. Immunization did not alter WBC composition. CONCLUSIONS: Manual WBC differentials are feasible in low-resource settings. WBC differentials are not affected by standard newborn immunization. However, the I:T ratio had no value in predicting subsequent development of sepsis.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".