Patients’ expectations of spine surgery for degenerative conditions: results from the Canadian Spine Outcomes and Research Network (CSORN)
Notice bibliographique
Résumé
© 2019 Elsevier Inc. BACKGROUND: Understanding patient expectations is a critical component of patient-centered care; however, little is known about which expectation(s) are most important to patients as they relate to their sense of postoperative success. PURPOSE: To investigate patient's preoperative expectations of change in symptoms, function, and well-being resulting from surgical intervention and to examine the associations between sociodemographic, lifestyle, health status, and clinical characteristics with patient outcome expectations STUDY DESIGN: Observational cross-sectional study. SAMPLE: Preoperative data from the Canadian Spine Outcomes and Research Network national registry of patients of patients (n=4,333) undergoing surgery for degenerative spinal conditions between 2012 and 2017. OUTCOME MEASURES: Patients reported their expectations as a result of the surgery (0 [no change], 1 [somewhat better], 2 [better] or 3 [much better]) for seven items: leg/arm pain, back/neck pain, independence in everyday activities, sporting activities/recreation, general physical capacity, frequency and quality of social contacts, and mental well-being. Patients also reported the single most important change expected. METHODS: Data on demographic, lifestyles, health status, clinical factors, and reasons for having surgery were also collected. Factor analysis was used to examine the multidimensionality of expectations. Multivariate linear regression was used to examine factors associated with expectations. RESULTS: Over 80% of patients reported expectation for improvements (at least somewhat better) in all items with the exception of social contacts (75.8%). Expectations are multidimensional; a two factor structure emerged indicating two expectation dimensions (pain relief and overall functional well-being). Two expectation scores were calculated corresponding to the two dimensions (0–100), with higher scores reflecting higher expectations. The mean±standard deviation pain relief expectation score was 78.5±24.7 and the mean overall functional well-being expectation score was 69.7±24.4. In multivariate analysis, the variables associated with these dimensions either differed or differed in degree of influence. For example, higher pain and disability scores, thoracolumbar location and diagnosis of spondylolisthesis were associated with higher expectations in both dimensions, while longer disease duration was only associated with lower overall functional well-being expectations. The top three most important expected change items were pain (improvement of leg or arm pain (29.1%)/improvement in back/neck pain (26.0%)), improvement in general capacity/function (21.0%), and improvement of independence in everyday activities (15.9%). Rankings of the most important expected change were similar across sociodemographic, lifestyle, health status, and clinical variables examined. CONCLUSIONS: Our findings highlight the need to identify and address specific individual expectations as part of the shared decision-making and presurgery education process.
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 | 0,002 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 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 ».