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Record W2329929706 · doi:10.5588/ijtld.14.0105

Chest radiography for active tuberculosis case finding in the homeless: a systematic review and meta-analysis

2014· review· en· W2329929706 on OpenAlexaff
Katryn Paquette, Matthew P. Cheng, Matthew Kadatz, Victoria Cook, Wenjia Chen, James C. Johnston

Bibliographic record

VenueThe International Journal of Tuberculosis and Lung Disease · 2014
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsCentre for Advancing Health OutcomesUniversity of British Columbia
Fundersnot available
KeywordsMedicineIncidence (geometry)TuberculosisTuberculinSputumPopulationMEDLINEMeta-analysisActive tuberculosisCochrane LibraryInternal medicineEnvironmental healthMycobacterium tuberculosisPathology

Abstract

fetched live from OpenAlex

SETTING: In low-incidence regions, tuberculosis (TB) often affects vulnerable populations. Guidelines recommend active case finding (ACF) in homeless populations, but there is no consensus on a preferred screening method. OBJECTIVE: We performed a systematic review and meta-analysis to evaluate the use of chest X-ray (CXR) screening in ACF for TB in homeless populations. DESIGN: Articles were identified through EMBASE, Medline and the Cochrane Library. Studies using symptom screens, CXRs, sputum sweeps, tuberculin skin tests and/or interferon-gamma release assays to detect active TB in homeless populations were sought. Data were extracted using a standardised method by two reviewers and validated with an objective tool. RESULTS: Sixteen studies addressing CXR screening of homeless populations for active TB in low-incidence regions were analysed. The pooled prevalence of active TB in the 16 study cohorts was 931 per 100 000 population screened (95%CI 565-1534) and 782/100 000 CXR performed (95%CI 566-1079). Six of seven longitudinal screening programs reported a reduction in regional TB incidence after implementation of the CXR-based ACF programme. CONCLUSION: Our data suggest that CXR screening is a good tool for ACF in homeless populations in low-incidence regions.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.519
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0070.005
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.076
GPT teacher head0.414
Teacher spread0.337 · 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 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

Citations28
Published2014
Admission routes1
Has abstractyes

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