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Record W2887660234 · doi:10.1111/tmi.13133

Risk of active tuberculosis among people with diabetes mellitus: systematic review and meta‐analysis

2018· review· en· W2887660234 on OpenAlexaboutno aff
Shintaro Hayashi, Daniel Chandramohan

Bibliographic record

VenueTropical Medicine & International Health · 2018
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMeta-analysisTuberculosisDiabetes mellitusCohort studyActive tuberculosisInternal medicineEnvironmental healthMycobacterium tuberculosisEndocrinologyPathology

Abstract

fetched live from OpenAlex

Abstract Objective To assess the risk of active TB in people with DM and the factors associated with this risk. Methods Systematic review and meta‐analysis. We searched the literature for studies that reported the effect of DM on TB controlled for the effect of age. Studies that had not established the diagnosis of DM prior to detecting active TB were excluded. Study quality was assessed by Newcastle‐Ottawa scale and we conducted a meta‐analysis using random‐effects models. Results Of 14 studies (eight cohort and six case–control studies) that involved 22 616 623 participants met the selection criteria and were included in the analysis. There was substantial variation between studies in the estimates of the effect of DM on TB . However, the pooled estimates from seven high‐quality studies showed that diabetic people have a 1.5‐fold increased risk of developing active TB vs . those without DM (95% CI 1.28–1.76), with relatively small heterogeneity ( I 2 = 44%). The increased risk of TB was observed predominantly among DM populations with poor glycaemic control. Conclusion There is evidence suggesting an increased risk of developing TB among people with DM , and that improving glycaemic control in DM patients would reduce the risk of developing TB . An integrated approach is needed to control the dual burden of DM and TB .

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)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.600
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0140.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.410
Teacher spread0.355 · 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.

Study designMeta-analysis
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

Citations106
Published2018
Admission routes1
Has abstractyes

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