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Record W2916229534 · doi:10.1289/isee.2017.2017-313

The Effect of Climate and Altitude Variability on Tuberculosis: A Systematic Review

2018· review· en· W2916229534 on OpenAlexaboutno aff
Yalemzewod Assefa Gelaw, Weiwei Yu, Soares Magalhães, Yibeltal Assefa, Gail Williams

Bibliographic record

VenueISEE Conference Abstracts · 2018
Typereview
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsnot available
Fundersnot available
KeywordsAltitude (triangle)TuberculosisEnvironmental healthClimate changeEffects of high altitude on humansEnvironmental scienceMedicineGeographyBiologyMeteorologyEcologyPathology

Abstract

fetched live from OpenAlex

Background/Aim: In 2015, a total of 10.4 million tuberculosis (TB) cases and 1.4 million associated deaths have been estimated to have occurred. Studies have suggested that changing climatic factors and altitude determine the geographical limits of TB. We undertook a systematic review of evidence for an association between meteorological factors and the risk of morbidity, drug-resistant (DR-TB) and death due to TB. Methods: A systematic review of the literature on the effect of climate and altitude on TB was performed using MOOSE guidelines. Electronic searches were undertaken from PubMed, EMBASE and Scopus. A quality score using the Newcastle-Ottawa scale for cross-sectional studies was attributed to assessing the strength of evidence on the association between climate and altitude and TB. A meta-analysis was performed on the association between altitude and TB morbidity. Results: We identified 17 studies, including two articles on DR-TB, one article on death due to TB and 14 articles on TB morbidity. These studies found changing climate and altitude were positively and/or negatively associated with the occurrence of TB morbidity, DR-TB and death. Conclusions: This review provides evidence for an association between TB morbidity and altitude and/or climate factors. TB control programs need to consider these factors in their strategies. However, there is limited evidence for the association between these factors and DR-TB and death from TB. More research is needed to estimate the contribution of these factors on TB infection, morbidity, drug-resistant and death and inform TB control strategies.

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)0.005
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.113
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.000
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 head0.031
GPT teacher head0.365
Teacher spread0.333 · 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 designSystematic review
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

Citations0
Published2018
Admission routes1
Has abstractyes

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