Epidemiology of neonatal tetanus in rural Pakistan.
Bibliographic record
Abstract
OBJECTIVES: To estimate the incidence of neonatal tetanus (NT) and to study the factors associated with NT mortality in Dadu district, Pakistan. METHODS: This study is a retrospective analysis of surveillance data from 1993-2003. NT cases were identified from the district surveillance database and hospital records reviews. Cases were ascertained using the NT standard case definition. Clinical records of all neonates (n = 416) admitted with the diagnosis of NT from 1993 through 2003 were reviewed for clinical presentation, progression and outcome. Rates, means and frequencies were calculated. Odds ratio was calculated to determine the association between potential risk factors and NT mortality. Logistic regression models were used to compute odds ratios and their associated 95% Confidence Intervals. RESULTS: Out of a total of 416 NT cases, 408 met the case definition. The overall case fatality rate (CFR) for NT was 30.1% (95% Confidence Interval (CI):25.6-34.6); CFR fell from 42% in 1993 to 29% in 2003 (p = 0.377). NT incidence decreased from 0.90/1000 live births (LB) in 1994 to 0.18/1000 LB in 2003. Multivariable analysis showed that age at admission of 8 days or less with {Odds Ratio (OR) 9.41, CI: 2.67-33.14} or without (OR 2.62, CI: 1.52-4.50) low neonatal weight was the strongest predictor of mortality. CONCLUSIONS: The rate of decline of neonatal tetanus incidence and case fatality was consistent with the impact of routine and supplementary immunization activities. In addition to strengthening maternal tetanus toxoid immunization coverage and hygienic delivery practices, health education focusing on increasing awareness of NT could help reduce NT mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".