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Record W2562821072 · doi:10.1093/cid/ciw836

Diabetes Mellitus and Latent Tuberculosis Infection: A Systemic Review and Metaanalysis

2016· review· en· W2562821072 on OpenAlexaboutno aff
Meng‐Rui Lee, Ya‐Ping Huang, Yu‐Ting Kuo, Chen-Hao Luo, Yun‐Ju Shih, Chin‐Chung Shu, Jann‐Yuan Wang, Jen‐Chung Ko, Chong‐Jen Yu, Hsien-Ho Lin

Bibliographic record

VenueClinical Infectious Diseases · 2016
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLatent tuberculosisOdds ratioDiabetes mellitusTuberculinConfidence intervalCohort studyTuberculosisMeta-analysisInternal medicineObservational studyCohortMycobacterium tuberculosisPathologyEndocrinology

Abstract

fetched live from OpenAlex

Background: Despite the well-documented association between diabetes and active tuberculosis, evidence of the association between diabetes and latent tuberculosis infection (LTBI) remains limited and inconsistent. Methods: We included observational studies that applied either the tuberculin skin test or the interferon gamma release assay for diagnosis of LTBI and that provided adjusted effect estimate for the association between diabetes and LTBI. We searched PubMed and EMBASE through 31 January 2016. The risk of bias of included studies was assessed using a quality assessment tool modified from the Newcastle-Ottawa scale. Results: Thirteen studies (1 cohort study and 12 cross-sectional studies) were included, involving 38263 participants. The cohort study revealed an increased but nonsignificant risk of LTBI among diabetics (risk ratio, 4.40; 95% confidence interval [CI], 0.50-38.55). For the cross-sectional studies, the pooled odds ratio from the random-effects model was 1.18 (95% CI, 1.06-1.30), with a small statistical heterogeneity across studies (I2, 3.5%). The risk of bias assessment revealed several methodological issues, but the overall direction of biases would reduce the positive causal association between diabetes and LTBI. Conclusions: Diabetes was associated with a small but statistically significant risk for LTBI. Findings from this review could be used to inform future cost-effectiveness analysis on the impact of LTBI screening programs among diabetics.

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)0.002
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.833
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.002
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.079
GPT teacher head0.436
Teacher spread0.357 · 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 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

Citations130
Published2016
Admission routes1
Has abstractyes

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