MétaCan
Menu
← Retour à la cohorte
Enregistrement W1576935381 · doi:10.1111/j.1532-5415.2005.53228_1.x

IMPROVING OSTEOPOROSIS MANAGEMENT IN AT‐RISK FRACTURE CLINIC PATIENTS

2005· letter· en· W1576935381 sur OpenAlexaffabout
Maureen C. Ashe, Heather McKay, Patricia A. Janssen, Pierre Guy, Karim M. Khan

Notice bibliographique

RevueJournal of the American Geriatrics Society · 2005
Typeletter
Langueen
DomaineMedicine
ThématiqueBone health and osteoporosis research
Établissements canadiensBC Children's HospitalVancouver General HospitalUniversity of British Columbia
Organismes subventionnairesnon disponible
Mots-clésMedicineOsteoporosisPsychological interventionIntervention (counseling)Physical therapyFamily medicinePediatricsInternal medicineNursing

Résumé

récupéré en direct d'OpenAlex

To the Editor: We would like to draw your attention to an important but preventable bone health and healthcare delivery dilemma that we believe can be improved with a simple, low-cost intervention. In 1982, researchers at the Mayo Clinic reported that history of a previous low-trauma or fragility fracture was associated with a 1.3 relative risk of subsequent hip fracture1 and this was likely an underestimation of the risk.2-4 In response, national guidelines in the United States and Canada emphasized that low-trauma fractures should not merely be treated orthopedically,5 but should also prompt a family physician evaluation to assess osteoporosis risk and manage it appropriately. Despite this ideal opportunity for secondary prevention of osteoporosis, numerous publications have highlighted the continual low investigation (∼20%) rates after a sentinel fracture.6–9 Even fewer patients received adequate therapy, highlighting the “knowledge-care gap.” In a review of the largest series of wrist fractures to date, only 23% of “at risk” patients were treated for osteoporosis,9 whereas one study10 reported from a retrospective review of a large database that only 9.8% of the women and 2.9% of the men were screened for osteoporosis. Yet another study11 suggests that screening for these “sentinel fractures” would reduce hip fractures 9%. Despite increasing awareness of this problem, few controlled studies have tested interventions to improve diagnosis and management. There have been recent publications that have tested interventions aimed at changing physician practice patterns.12 Recently, a simple four-part intervention using a diagnosis/management questionnaire and counting “osteoporosis best practices” offered by the family physician to patients with a wrist fragility fracture was tested.12 Participants who volunteered for this study were allocated into one of two groups (intervention or control). Each participant in the intervention group received an information sheet explaining that she or he had suffered a low-trauma fracture diagnostic of osteoporosis, a request to take a letter from the orthopedic surgeon who was managing the fracture to show to the family physician alerting her or him to the recent low-trauma fracture, and a follow-up telephone call at 4 to 6 weeks to remind the participant to visit her or his family physician. The fourth element of the intervention was a facsimile from the orthopedic surgeon to the family physician specifically requesting general practitioner assessment and management of osteoporosis. In the control group, participants were not given any additional information on risk of osteoporosis by the researchers. Control group patients received usual care, defined as treatment for the fracture by the hospital staff and routine notification to the family physician of the fracture and any follow-up plans. Both groups were telephoned at 6 months to administer the diagnosis management questionnaire. This questionnaire was developed specifically to ascertain the osteoporosis investigation rate and osteoporosis “best practices” as recommended by the 2002 Osteoporosis Consensus.5 The original distal radius fracture “WristWatch” study was expanded to include humeral fractures. Sixty-two participants were enrolled in this study (36 control and 26 intervention). (See Table 1 for participant characteristics.) In this multicomponent quality-improvement intervention of a patient-education and physician-alerting system, 73% of participants in the intervention group were investigated for osteoporosis. The absolute difference was 54%, and the relative risk was 3.8 (95% confidence interval=1.9–7.6) Once again, investigation for osteoporosis was significantly higher in the intervention group (P<.001). It is tempting to speculate that there might be at least two ways that this study could be translated into clinical practice. Standing orders contained within the orthopedic surgeon's follow-up note to the family physician would ensure that patients and family physicians instigate optimum osteoporosis care. Alternatively, a healthcare worker could liaise between potential patients at risk and the healthcare services to initiate the care pathway (fracture liaison service). One group of providers13 tested such a model in their clinical practice, and a designated team member performed case finding and initiated appropriate investigation. This can be an effective service for larger facilities with adequate funding. Although it can be seen as the criterion standard, there are people and regions who are not able to access such services. Although the current study showed that family physicians investigated at-risk patients for osteoporosis when an orthopedic surgeon prompted them to do so, future studies could investigate the use of clinical pathways that use the expertise of other health professionals (e.g., physical therapist, nurse) at the fracture clinic or emergency department level to systematically initiate family physician follow-up regarding osteoporosis. Osteoporosis is a serious medical condition and at-risk patients need to be identified. A fragility fracture is a sentinel event and can be used to effectively initiate investigation. A simple intervention can influence physician behaviors. Larger studies should further refine this intervention by testing the effectiveness of its individual components.

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,041
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: aucune
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,030

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,041
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0020,002
Science ouverte0,0030,001
Intégrité de la recherche0,0090,010
Charge utile insuffisante (le modèle a refusé de juger)0,0090,002

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.

Tête enseignante Opus0,012
Tête enseignante GPT0,302
Écart entre enseignants0,289 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations8
Publié2005
Routes d'admission2
Résumé présentoui

Explorer davantage

Même revueJournal of the American Geriatrics Society→Même sujetBone health and osteoporosis research→Travaux en français237 207→