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Record W1986884184 · doi:10.1016/j.ijid.2011.04.011

Completeness of reporting and case ascertainment for neonatal tetanus in rural Pakistan

2011· article· en· W1986884184 on OpenAlexaff
Jonathan Lambo, Zahid Hussain Khahro, Mahmood Iqbal Memon, Muhammad Ismail Lashari

Bibliographic record

VenueInternational Journal of Infectious Diseases · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicDiphtheria, Corynebacterium, and Tetanus
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsCompleteness (order theory)Neonatal tetanusTetanusMedicinePediatricsMathematicsVirologyVaccination

Abstract

fetched live from OpenAlex

OBJECTIVES: The objectives of this study were to assess the case ascertainment and completeness of neonatal tetanus (NT) reporting and to estimate the incidence of NT in Dadu District, Pakistan. METHODS: We conducted active surveillance and hospital record reviews for suspected NT cases. We compared the cases of NT reported to the routine surveillance system with the cases identified through the hospital record reviews for 1993 through 2003. The two-source capture-recapture method was used to evaluate case ascertainment in the routine surveillance system and to estimate the incidence of cases of NT. RESULTS: Active surveillance and hospital record reviews identified 134 cases in addition to 274 cases in the routine surveillance system. The two-source capture-recapture method indicated that there would have been 463 cases during this period (95% confidence interval (CI)=418-508), representing an average annual incidence of 0.62 per 1000 live-births. The overall completeness of routine reporting was 59.2%. The proportions of cases reported were 68.1% for government hospitals and 53.8% for private reporting sites. CONCLUSIONS: Reporting of NT cases is incomplete. Active promotion of private sector participation, community involvement, and strengthening of the government sector as a way of improving NT reporting and surveillance is strongly suggested.

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)0.000
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 score0.055
Threshold uncertainty score0.310

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.017
GPT teacher head0.294
Teacher spread0.278 · 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

Citations14
Published2011
Admission routes1
Has abstractyes

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