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Record W2779995263 · doi:10.1136/bmjresp-2017-000250

International perception of lung sounds: a comparison of classification across some European borders

2017· article· en· W2779995263 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBMJ Open Respiratory Research · 2017
Typearticle
Languageen
FieldMedicine
TopicPhonocardiography and Auscultation Techniques
Canadian institutionsUniversity of Manitoba
FundersUniversitetet i Tromsø
KeywordsCracklesMedicineAuscultationKappaStethoscopeCohen's kappaHeart soundsLungAudiologyRespiratory soundsCardiologyRadiologyInternal medicineAsthmaStatisticsMathematics

Abstract

fetched live from OpenAlex

INTRODUCTION: Lung auscultation is helpful in the diagnosis of lung and heart diseases; however, the diagnostic value of lung sounds may be questioned due to interobserver variation. This situation may also impair clinical research in this area to generate evidence-based knowledge about the role that chest auscultation has in a modern clinical setting. The recording and visual display of lung sounds is a method that is both repeatable and feasible to use in large samples, and the aim of this study was to evaluate interobserver agreement using this method. METHODS: With a microphone in a stethoscope tube, we collected digital recordings of lung sounds from six sites on the chest surface in 20 subjects aged 40 years or older with and without lung and heart diseases. A total of 120 recordings and their spectrograms were independently classified by 28 observers from seven different countries. We employed absolute agreement and kappa coefficients to explore interobserver agreement in classifying crackles and wheezes within and between subgroups of four observers. RESULTS: When evaluating agreement on crackles (inspiratory or expiratory) in each subgroup, observers agreed on between 65% and 87% of the cases. Conger's kappa ranged from 0.20 to 0.58 and four out of seven groups reached a kappa of ≥0.49. In the classification of wheezes, we observed a probability of agreement between 69% and 99.6% and kappa values from 0.09 to 0.97. Four out of seven groups reached a kappa ≥0.62. CONCLUSIONS: The kappa values we observed in our study ranged widely but, when addressing its limitations, we find the method of recording and presenting lung sounds with spectrograms sufficient for both clinic and research. Standardisation of terminology across countries would improve international communication on lung auscultation 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.

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)0.001
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 score0.411
Threshold uncertainty score0.335

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.384
GPT teacher head0.605
Teacher spread0.222 · 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