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Record W4391385717 · doi:10.2196/47422

Timely Pulmonary Tuberculosis Diagnosis Based on the Epidemiological Disease Spectrum: Population-Based Prospective Cohort Study in the Republic of Korea

2024· article· en· W4391385717 on OpenAlexvenueno aff
Yousang Ko, Jae Seuk Park, Jinsoo Min, Hyung Woo Kim, Hyeon‐Kyoung Koo, Jee Youn Oh, Yun‐Jeong Jeong, Eun Hye Lee, Bumhee Yang, Ju Sang Kim, Sung-Soon Lee, Yunhyung Kwon, Jiyeon Yang, Ji yeon Han, You Jin Jang, Jinseob Kim

Bibliographic record

VenueJMIR Public Health and Surveillance · 2024
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
FundersKorea Disease Control and Prevention Agency
KeywordsEpidemiologyMedicineTuberculosisDiseaseProspective cohort studyEnvironmental healthCohort studyPulmonary tuberculosisCohortPopulationPublic healthIntensive care medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Timely pulmonary tuberculosis (PTB) diagnosis is a global health priority for interrupting transmission and optimizing treatment outcomes. The traditional dichotomous time-divided approach for addressing time delays in diagnosis has limited clinical application because the time delay significantly varies depending on each community in question. OBJECTIVE: We aimed to reevaluate the diagnosis time delay based on the PTB disease spectrum using a novel scoring system that was applied at the national level in the Republic of Korea. METHODS: The Pulmonary Tuberculosis Spectrum Score (PTBSS) was developed based on previously published proposals related to the disease spectrum, and its validity was assessed by examining both all-cause and PTB-related mortality. In our analysis, we integrated the PTBSS into the Korea Tuberculosis Cohort Registry. We evaluated various time delays, including patient, health care, and overall delays, and their system-associated variables in line with each PTBSS. Furthermore, we reclassified the scores into distinct categories of mild (PTBSS=0-1), moderate (PBTBSS=2-3), and severe (PBTBSS=4-6) using a multivariate regression approach. RESULTS: Among the 14,031 Korean patients with active PTB whose data were analyzed from 2018 to 2020, 37% (n=5191), 38% (n=5328), and 25% (n=3512) were classified as having a mild, moderate, and severe disease status, respectively, according to the PTBSS. This classification can therefore reflect the disease spectrum of PTB by considering the correlation of the score with mortality. The time delay patterns differed according to the PTBSS. In health care delays according to the PTBSS, greater PTB disease progression was associated with a shorter diagnosis period, since the condition is microbiologically easy to diagnose. However, with respect to patient delays, the change in elapsed time showed a U-shaped pattern as PTB progressed. This means that a remarkable patient delay in the real-world setting might occur at both apical ends of the spectrum (ie, in both mild and severe cases of PTB). Independent risk factors for a severe PTB pattern were age (adjusted odds ratio 1.014) and male sex (adjusted odds ratio 1.422), whereas no significant risk factor was found for mild PTB. CONCLUSIONS: Timely PTB diagnosis should be accomplished. This can be improved with use of the PTBSS, a simple and intuitive scoring system, which can be more helpful in clinical and public health applications compared to the traditional dichotomous time-only approach.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
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 head0.047
GPT teacher head0.361
Teacher spread0.314 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations1
Published2024
Admission routes1
Has abstractyes

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