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Record W2904993390 · doi:10.12968/ijtr.2018.25.12.648

Reliability and validity of non-radiological measures of thoracic kyphosis in chronic obstructive pulmonary disease

2018· article· en· W2904993390 on OpenAlexaff
Annemarie L. Lee, Roger Goldstein, Matthew Rhim, Christen Chan, Dina Brooks, Karl Zabjek

Bibliographic record

VenueInternational Journal of Therapy and Rehabilitation · 2018
Typearticle
Languageen
FieldMedicine
TopicScoliosis diagnosis and treatment
Canadian institutionsWest Park Healthcare CentreUniversity of Toronto
FundersJohns Hopkins University
KeywordsMedicineKyphosisRadiographyRadiological weaponPulmonary diseasePhotogrammetryRadiologyValidityReliability (semiconductor)OrthodonticsInternal medicineArtificial intelligencePsychometrics

Abstract

fetched live from OpenAlex

Background/Aims: Thoracic kyphosis in people with chronic obstructive pulmonary disease can be measured from digital photogrammetry or three-dimensional motion capture. This study aimed to determine the reliability, validity and agreement for non-radiological measures of thoracic kyphosis in chronic obstructive pulmonary disease. Methods: A total of 19 participants with chronic obstructive pulmonary disease were included. Cobb angles from chest radiographs and spinous process landmarks using photogrammetry and three-dimensional motion capture were evaluated. Findings: The mean kyphosis (± standard deviation) was 48.8 ± 10.9 degrees by radiograph; 49.6 ± 12.9 degrees by three-dimensional motion capture and 52.2 ± 11.1 degrees by photogrammetry. Radiographic Cobb angle and photogrammetry measurements demonstrated excellent intra- and inter-rater reliability. Correlation between non-radiological kyphosis measurements and chest radiographs was strong (Pearson's r 2 >0.75 for both). Limits of agreement between radiographs and 3D motion capture were –9 degrees to 7 degrees, and –12 to 8 degrees between radiographs and photogrammetry. Conclusions: Non-radiological measures of thoracic kyphosis are reliable and valid in chronic obstructive pulmonary disease.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.059
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.045
GPT teacher head0.363
Teacher spread0.318 · 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 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

Citations4
Published2018
Admission routes1
Has abstractyes

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