Osteoporotic Vertebral Fracture Prevalence Varies Widely
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».