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Record W3202169689 · doi:10.3390/vaccines9101133

Oral Polio Vaccine Campaigns May Reduce the Risk of Death from Respiratory Infections

2021· article· en· W3202169689 on OpenAlexfundno aff
Sebastian Nielsen, Hasan Mahmud Sujan, Christine Stabell Benn, Peter Aaby, Syed Manzoor Ahmed Hanifi

Bibliographic record

VenueVaccines · 2021
Typearticle
Languageen
FieldImmunology and Microbiology
TopicImmune responses and vaccinations
Canadian institutionsnot available
FundersSeventh Framework ProgrammeNovo Nordisk FondenSveriges RegeringRådet for UdviklingsforskningGovernment of CanadaMinistry of Science and Technology, Government of the People’s Republic of BangladeshGovernment of the United Kingdom
KeywordsPoliomyelitisMedicineRespiratory systemIntensive care medicineVirologyEnvironmental healthInternal medicine

Abstract

fetched live from OpenAlex

Oral polio vaccine (OPV) campaigns, but not other campaigns, have been associated with major reductions in child mortality. Studies have shown that OPV reduces the risk of respiratory infections. We analysed the causes of death at 0–2 years of age in Chakaria, a health and demographic surveillance Systems in Bangladesh, in the period 2012–2019 where 13 national campaigns with combinations of OPV (n = 4), vitamin A supplementation (n = 9), measles vaccine (MV) (n = 2), and albendazole (n = 2) were implemented. OPV-only campaigns reduced overall mortality by 30% (95% confidence interval: −10–56%). Deaths from respiratory infections were reduced by 62% (20–82%, p = 0.01) in the post-neonatal period (1–35 months), whereas there was as slight increase of 19% (−37–127%, p = 0.54) for deaths from other causes. There was no benefit of other types of campaigns. Hence, the hypothesis that OPV may have beneficial non-specific effects, protecting particularly against respiratory infections, was confirmed.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
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)0.0030.000

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 head0.023
GPT teacher head0.286
Teacher spread0.263 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2021
Admission routes1
Has abstractyes

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