Neonatal tetanus incidence in Dadu District, Pakistan, 1993–2003
Bibliographic record
Abstract
BACKGROUND: The study objective was to assess the incidence of neonatal tetanus (NT) in Dadu District, Pakistan. METHODS: We analyzed the NT surveillance data for the period 1993-2003 in order to determine the NT incidence. We identified unreported NT cases retrospectively in 2005, by active surveillance and hospital record reviews. The 2-source capture-recapture method was used to estimate the incidence of NT cases. RESULTS: Active methods identified 134 cases in addition to 274 cases in the routine surveillance system. The average annual incidence in routine surveillance was 0.55 per 1000 live-births (LB). Based on an estimated 463 NT cases during this period (95% confidence interval (CI) 425-509), the average annual incidence (capture-recapture) was 0.62 per 1000 LB. Through routine immunization and supplementary immunization activities, NT incidence declined from 0.87 per 1000 LB in 1994 to 0.18 per 1000 LB in 2003. Males had higher incidence rates than females. Both of the average annual incidence rates (by routine surveillance and capture-recapture method) are below the World Health Organization global elimination goal. CONCLUSION: Enhanced case-based investigation, targeted tetanus toxoid immunization of women of childbearing age, community-based NT reporting, immediate reporting and active surveillance are critical to sustain the ongoing decline in NT incidence.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".