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Record W1983439971 · doi:10.1016/j.ijgo.2012.07.030

Disposable clean delivery kits and prevention of neonatal tetanus in the presence of skilled birth attendants

2012· article· en· W1983439971 on OpenAlexaff
Syed Ahsan Raza, Bilal Iqbal Avan

Bibliographic record

VenueInternational Journal of Gynecology & Obstetrics · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiphtheria, Corynebacterium, and Tetanus
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineTetanusNumber needed to treatNeonatal tetanusConfidence intervalOdds ratioToxoidPopulationPediatricsObstetricsRelative riskEnvironmental healthImmunologyInternal medicineVaccination

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score0.284

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.010
GPT teacher head0.260
Teacher spread0.250 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations20
Published2012
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Gynecology & ObstetricsSame topicDiphtheria, Corynebacterium, and TetanusFrench-language works237,207