MétaCan
Menu
Back to cohort
Record W4212937399 · doi:10.3201/eid2803.211919

Retrospective Cohort Study of Effects of the COVID-19 Pandemic on Tuberculosis Notifications, Vietnam, 2020

2022· article· en· W4212937399 on OpenAlexaboutno aff
Tasnim Hasan, Viet Nhung Nguyen, Hoa Binh Nguyen, Thu Anh Nguyen, Hien Thi Thu Le, Cuong D. Pham, Nam Hoang, Thi Mai Phuong Nguyen, Justin Beardsley, Guy B. Marks, Greg J. Fox

Bibliographic record

VenueEmerging infectious diseases · 2022
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
Fundersnot available
KeywordsTuberculosisPandemicMedicineQuarter (Canadian coin)Retrospective cohort studyCoronavirus disease 2019 (COVID-19)Cohort studyCohortMulti-drug-resistant tuberculosisVirologyDiseaseInternal medicineMycobacterium tuberculosisInfectious disease (medical specialty)GeographyPathology

Abstract

fetched live from OpenAlex

severe acute respiratory syn- drome coronavirus 2 (SARS-CoV-2) has been causing a global coronavirus disease pandemic that has had wide-reaching effects on delivery of care for many other health conditions, including tuberculosis (TB). Each year, 10 million TB cases are diagnosed and 1.5 million TB deaths occur worldwide (1). The World Health Organization (WHO) has identifi ed substantial effects of the COVID-19 pandemic on TB control efforts (1). By late 2020, substantial reductions in TB case notifi cations were evident in both high-and middle-income countries (2-6), including countries where COVID-19 had been well-controlled (7). Decreased TB notifi cations led to fears that delays in case detection and reduced treatment completion resulting from the COVID-19 pandemic might lead to increased Mycobacterium tuberculosis transmission and consequently higher mortality rates (8). Indeed, evidence suggests that the COVID-19 pandemic has resulted in reduced patient adherence to treatment (9), decreased access to medications (10,11), delayed access to services (10,12), and higher rates of loss to follow-up for patients with TB (10). Some of this disruption has been attributed to diversion of resources and interruptions to drug supply and delivery resulting from the COVID-19 pandemic (13). Furthermore, some persons with TB have avoided seeking healthcare because of fear of acquiring . In addition, evidence from South Africa suggests that outcomes for SARS-CoV-2 infection are worse for patients co-infected with TB (15).

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.700

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.018
GPT teacher head0.328
Teacher spread0.310 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations23
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueEmerging infectious diseasesSame topicTuberculosis Research and EpidemiologyFrench-language works237,207