Prognostic factors for mortality in neonatal tetanus: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: To determine prognostic factors for mortality in neonates with tetanus and to assess the associations between prognostic factors and neonatal tetanus (NT) mortality. METHODS: Five databases were searched for studies on prognostic factors and NT mortality published up to April 2013 to identify studies relevant to this review. Prognostic factors of interest were birth weight, age at onset of symptoms, age at presentation, delay in presentation, and duration of hospitalization. Odds ratios (ORs) for prognostic factors and mortality were estimated by random effects models and stratified analyses for all studies. RESULTS: Sixteen studies including a total of 4535 neonates were included in the analysis: nine from Africa, five from Asia, and two from Europe. The prognostic factors identified consistently in the studies were birth weight, age at onset of symptoms, and age at presentation. Of the 16 studies, only one assessed all three prognostic factors, five studies assessed two prognostic factors, and 10 studies assessed one prognostic factor. Neonates with a low birth weight were more likely to have an increased odds of NT death (OR 2.09, 95% confidence interval (CI) 1.29-3.37) than normal weight neonates. This mortality risk was exacerbated for low birth weight neonates with age at onset≤6 days (OR 6.80, 95% CI 2.42-19.11). Age at onset≤5-7 days was associated with an increased odds of NT death. CONCLUSIONS: Low birth weight predicted an increased odds of death by NT. Age at onset≤5-7 days to diagnosis is crucial in determining survival among neonates with tetanus.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".