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Record W2811360459 · doi:10.1002/jbmr.3541

Osteoporotic Vertebral Fracture Prevalence Varies Widely

2018· letter· en· W2811360459 on OpenAlexaboutno aff
Nigel Gilchrist, John Quayle, Greg Hamilton, Lynda Te Momo

Bibliographic record

VenueJournal of Bone and Mineral Research · 2018
Typeletter
Languageen
FieldEngineering
TopicMedical Imaging and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsRadiological weaponMedicineOsteoporosisRadiographyGold standard (test)TerminologyPhysical therapyRadiologyPathology

Abstract

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To The Editor, In a recent issue of JBMR, we read with interest “Osteoporotic Vertebral Fracture Prevalence Varies Widely Between Qualitative and Quantitative Radiological Assessment Methods: The Rotterdam Study” by Oei and colleagues1 and “Comparative Analysis of Radiology of Osteoporotic Vertebral Fractures in Women and Men” by Lentle and colleagues,2 as well as the Commentary that follows these two articles by Pawel Szulc.3 Oei and colleagues point out the different methods for the radiological assessment of vertebral fractures, and comment on the lack of a gold standard. They used existing radiological images and two different types of analysis to distinguish between significant and mild vertebral deformities. Lentle and colleagues, on the other hand, comment on cross-sectional longitudinal observations from the Canadian Multicentre Osteoporosis Study. They too compared two methods for the osteoporotic vertebral fracture assessment of existing and lateral spine radiographs and concluded that defining vertebral fractures by mABQ (modified algorithm-based qualitative approach) is preferred to the GSQ (Genant semiquantitative technique) for clinical assessments. The authors of both studies used large groups of patients and undertook a time- and resource-intensive analysis.1, 2 Pawel Szulc further comments that the identification of vertebral fractures is a major health problem and suffers from misleading and inaccurate terminology used in radiological reports. In New Zealand, we took a different approach to our patient analysis. We used existing images of patients, available electronically through a shared repository imported to HCAS (Healthcare Analytic Systems). The Canterbury District Health Board has legislative obligations to monitor the delivery and performance of services, as well as to provide information to the Ministry of Health, including identifying information essential for the purposes for which the information is sought. This is designed to assess whether patients with fractures are receiving appropriate care. We then used a clinical health ontology: SNOMED CT (https://www.snomed.org/). SNOMED is a comprehensive and precise clinical health terminology product that enables us to search and codify terms and small sentences within clinical notes and radiological reports; it incorporates words or a combination of words such as anterior, wedge, fracture, vertebra, closed, thoracic, lumbar, or collapse. We used this software to extract those individuals with clearly identified thoracic and lumbar vertebral fractures (https://www.health.govt.nz/nz-health-statistics/classification-and-terminology/new-zealand-snomed-ct-national-release-centre/snomed-ct). These were then visually reviewed online and appropriate therapy and or investigation instituted. Using this approach, we identified all vertebral fractures: Approximately 30% of patients were already on some form of treatment or were being investigated; 30% were not on any treatment and were followed up by the Fracture Liaison Service with appropriate investigations, radiology, and treatment; another approximately 30% were deemed not to have osteoporotic vertebral fractures based on a number of additional clinical factors or because of wrong classification. This approach was endorsed by New Zealand's Health Information Standard Organisation (https://www.health.govt.nz/our-work/ehealth/digital-health-sector-architecture-standards-and-governance/health-information-standards) as the clinical terminology to be used across the health and disability sector within New Zealand. The use of digital images and clinical notes in our health system has streamlined the approach to diagnosing and treating those people with vertebral fractures without the use of significant additional resources. All authors state they have no conflicts of interest.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.458
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
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 head0.034
GPT teacher head0.312
Teacher spread0.278 · 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 designNot applicable
Domainnot available
GenreCommentary

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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