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Limitations of Chest Radiography in Diagnosing Subclinical Pulmonary Tuberculosis in Canada

2023· article· en· W4367046354 on OpenAlexafffundabout
Richard Long, Angela Lau, J Barrié, Christopher Winter, Gavin Armstrong, Mary Lou Egedahl, Alexander Doroshenko

Bibliographic record

VenueMayo Clinic Proceedings Innovations Quality & Outcomes · 2023
Typearticle
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsUniversity of Alberta
FundersAlberta Health Services
KeywordsChest radiographSubclinical infectionMedicineAbnormalityTuberculosisRadiologyGold standard (test)Context (archaeology)RadiographySputumLungMycobacterium tuberculosisInternal medicinePathology

Abstract

fetched live from OpenAlex

that does not cause TB-related symptoms but does cause other abnormalities that can be detected using existing radiologic and mycobacteriologic assays." In high-income countries, subclinical PTB is usually diagnosed during active case finding, is acid-fast bacilli smear negative, and associated with minimal or no lung parenchymal abnormality on chest radiograph. In the absence of symptoms, the epidemiologic risk of TB and chest radiograph are critical to making the diagnosis. In a cohort of 327 patients with subclinical PTB, we address the question-how well field radiologists perform at identifying features important to the diagnosis of PTB, the presence or absence of which have been established by a panel of expert radiologists? Although not performing badly compared with this "gold standard," field readers were nevertheless susceptible to overread or underread films and miss key diagnostic features, such as the presence of a lung parenchymal abnormality, typical pattern, or cavitation. In the context of active case finding during which most patients with subclinical PTB are discovered, limitations of the chest radiograph need to be recognized, and sputum, ideally induced, should be submitted regardless of the radiographic findings.

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)0.031
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.008
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.238
GPT teacher head0.425
Teacher spread0.187 · 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 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

Citations5
Published2023
Admission routes3
Has abstractyes

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