Disposable clean delivery kits and prevention of neonatal tetanus in the presence of skilled birth attendants
Bibliographic record
Abstract
OBJECTIVE: To determine whether the use of disposable clean delivery kits (CDKs) is effective in reducing neonatal tetanus (NNT) infection, regardless of the skills of birth attendants in resource-poor settings. METHODS: A secondary analysis was conducted on data from a matched case-control study in Karachi, Pakistan, involving 140 NNT cases and 280 controls between 1998 and 2001. Conditional logistic regression was performed to assess the independent effect on NNT of CDKs and skilled birth attendants (SBAs). RESULTS: After adjustment for socioeconomic factors, both CDKs (adjusted matched odds ratio [mOR] 2.0; 95% confidence interval [CI], 1.3-3.1) and SBAs (adjusted mOR 1.7; 95% CI, 1.1-2.7) were independently associated with NNT. The association with CDKs remained significant when additionally adjusted for SBAs (mOR 2.0; 95% CI, 1.0-3.9; P=0.05). The population attributable risk for lack of CDK use was 24% in the study setting. CONCLUSION: In the context of resource-poor settings in low-income countries with poor coverage of tetanus toxoid immunization, the use of CDKs seems to be an effective strategy for reducing NNT infection, irrespective of the skill levels of birth attendants. Approximately one-quarter of NNT cases could be prevented in low-income populations with the use of CDKs.
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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 | 0.001 | 0.010 |
| 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) | 0.001 | 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".