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Record W2939407080 · doi:10.3390/tropicalmed4020059

Bacterial Resistance in Pneumonia in Developing Countries—A Role for Iron Chelation

2019· review· en· W2939407080 on OpenAlexaff
Sufia Islam, Mohammod Jobayer Chisti, Muniruddin Ahmed, Nafiza Anwar, Christine Lehmann

Bibliographic record

VenueTropical Medicine and Infectious Disease · 2019
Typereview
Languageen
FieldMedicine
TopicIron Metabolism and Disorders
Canadian institutionsDalhousie University
Fundersnot available
KeywordsStreptococcus pneumoniaeSepsisPneumoniaMedicineBacterial pneumoniaImmune systemMortality rateIncidence (geometry)ImmunologyHaemophilus influenzaeIntensive care medicineInternal medicineMicrobiologyAntibioticsBiology

Abstract

fetched live from OpenAlex

Pneumonia represents one of the major infectious diseases in developing countries and is associated with high mortality, in particular in children under the age of five. The main causative bacterial agents are Streptococcus pneumoniae and Haemophilus influenzae type B, accounting for 33% and 16%, respectively, of the mortality in under-fives. Iron modulates the immune response in infectious diseases and increased iron levels can lead to complications such as sepsis and multiorgan failure. This review will look into the use of iron chelators in order to reduce microbial growth and attenuate a dysregulated immune response during infection. Our hypothesis is that temporary restriction of iron will lessen the incidence and complication rate of infections like pneumonia and result in a decrease of mortality and morbidity.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.319
Teacher spread0.294 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations2
Published2019
Admission routes1
Has abstractyes

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