SUN-374 Decisions to Accept or Decline Pharmacologic Osteoporosis Therapy After Attending a Novel Patient-Centred Educonsult Program for Osteoporosis (PEP-OP)
Notice bibliographique
Résumé
Abstract Osteoporosis affects >200 million people, resulting in >8.9 million annual fragility fractures worldwide. Available medications can reduce fracture risk by 40–60%, although access to specialty osteoporosis services is limited, and many individuals remain unaware of their fracture risk and their treatment options. As the one-on-one ‘traditional consultation’ (TC) model of osteoporosis care is not time efficient (i.e. a single TC often requires >45 minutes), there is a need to identify innovative consultative models that can improve accessibility to osteoporosis care while maintaining quality. At our Osteoporosis Centre, we have implemented a group counseling model for this purpose: the Patient-Centred Educonsult Program for Osteoporosis (PEP-OP). Each two-hour PEP-OP session - co-facilitated by an osteoporosis physician and a nurse - provides up to 10 patients (the equivalent to 3–5 half-day physician clinics under the TC model) with a combined consultative and educational experience consisting of an individualized fracture risk assessment and extensive review of medications available to lower fracture risk. Patients are then encouraged to make an informed, autonomous decision about osteoporosis treatment initiation. Although the PEP-OP can accommodate a greater patient volume than the TC, and we have previously reported that the PEP-OP results in high patient satisfaction, it is not known whether PEP-OP produce similar results compared to TC in terms of treatment decisions. In this cohort study, we compared decisions to initiate osteoporosis therapy in PEP-OP (N=100) and TC (N=43) attendees. Ten-year risk of major osteoporotic fracture was estimated for each participant using the FRAX calculator, and participants were stratified based on whether their ten-year risk was ≥20% or <20%. Proportion of participants in each risk category who decided to initiate treatment were compared between the PEP-OP and TC groups. PEP-OP and TC groups were comparable in terms of age (63.3 vs 64.9 years), BMI (24.4 vs 24.9 kg/m2), previous fragility fractures (35 vs 25%), parental hip fractures (19 vs 23%), lumbar neck T-score (-2.5 vs -2.3), femoral neck T-score (-2.1 vs -2.1) and average FRAX estimate (13.1 vs 13.3%). The proportion of participants at high ten-year risk of major osteoporotic fracture (≥20%) who decided to initiate treatment was similar in both the PEP-OP (7/16, 44%) and TC (5/10, 50%) groups, according to the Chi Square Test (p=0.76). Among those with FRAX estimate of <20%, a similar proportion of patients in the PEP-OP (15/84, 18%) and TC (4/33, 12%) groups chose to undergo treatment (X2, p=0.45). In summary, decisions to initiate pharmacologic therapy were similar for the PEP-OP and the TC. Considering that the PEP-OP is acceptable to patients and is more efficient than the TC, this care model should be considered by other centers wishing to improve access to high-quality osteoporosis care.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,033 | 0,002 |
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.
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 source (Gemma direct ou Codex distillé), 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 ».