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Record W2134318316 · doi:10.1016/j.jocd.2015.02.004

Relationship Between Vertebral Fracture Burden, Height Loss, and Pulmonary Function in Postmenopausal Women With Osteoporosis

2015· article· en· W2134318316 on OpenAlexaff
John H. Krege, David L. Kendler, Kelly Krohn, Harry K. Genant, Jahangir Alam, Pierre‐Yves Berclaz, Barbara J. Coffey, Corina Loghin

Bibliographic record

VenueJournal of Clinical Densitometry · 2015
Typearticle
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineOsteoporosisSpirometryPulmonary function testingDeformityVital capacitySpinal fractureOswestry Disability IndexInternal medicineLung functionSurgeryLungLow back painDiffusing capacityPathology

Abstract

fetched live from OpenAlex

The purpose of this analysis was to assess the association of osteoporosis-related vertebral fracture burden and pulmonary function. This study also examined the relationship between vertebral fracture burden and height loss, estimated by arm span - height. This was a single-site and single-visit study. Patients had a history of at least 1 moderate or severe vertebral fracture. Vertebral fracture burden was quantified using the spinal deformity index (SDI). Pulmonary function during inspiration was determined by spirometry. Forty-one women aged 70-91 completed the study. Vertebral fracture burden negatively correlated with forced inspiratory vital capacity and inspiratory time. For each unit increase in SDI, forced inspiratory vital capacity decreased by 1.62%, and inspiratory time decreased by 2.39%. There was no correlation between SDI and measures of inspiratory flow. For each unit increase in SDI, height decreased by about 0.5 cm. Vertebral fractures were associated with decreased lung volume and height loss.

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)0.000
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.081
GPT teacher head0.400
Teacher spread0.319 · 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

Citations58
Published2015
Admission routes1
Has abstractyes

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