Notice bibliographique
Résumé
Commentary The PROMIS (Patient-Reported Outcomes Measurement Information System) is a widely used outcome measure that is administered using computerized adaptive testing. As patient-reported outcome measures (PROMs) evolve, clinicians and investigators need to consider whether statistically significant changes in them are clinically important for patients. This paper helps to determine whether changes in the PROMIS outcomes after total ankle replacement (TAR) are clinically important by identifying the patient acceptable symptom state, or PASS. The PASS is the symptom threshold beyond which patients consider themselves well. Patient satisfaction is not achieved simply by obtaining a certain level of pain relief or functional improvement, and it can also vary depending on patient expectations as well as the diagnosis and procedure. Surgeons should therefore aim to ensure that patients have realistic expectations for TAR outcomes. This study highlights the strong association between postoperative PROMIS pain scores and the ability to achieve the PASS, showing the importance of pain reduction in patient satisfaction. Furthermore, this study identifies several preoperative variables that may affect achievement of the PASS. Patients with better preoperative physical function and mental health scores were more likely to achieve the PASS for physical function postoperatively. Patients with prior surgery, diabetes, or peripheral vascular disease were less likely to achieve the PASS for physical function. However, the PASS value can vary depending on the anchor question. There is no gold standard to capture patient satisfaction, and anchor questions with different wording might result in different thresholds. An anchor question focused on pain will not be relevant to a patient concerned about function. Thus, multiple anchors may be used to evaluate the PASS. The anchor questions used in this study specifically asked about satisfaction with the surgery and about the acceptability of current foot and ankle symptoms and function. However, the PROMs that they used were the PROMIS Physical Function (V1.2), Pain Interference (V1.1), Pain Intensity (3a, V1.0), Global Physical Health, Global Mental Health, and Depression (V1.0). None of these PROMs are foot and ankle-specific. In Figure 1, the graph shows very little change, particularly for Global Physical Health, Global Mental Health, and Depression, from before to after surgery1. So, what is the relevance of satisfaction with surgery and of foot and ankle symptoms for these outcomes? Would foot and ankle-specific PROMs result in different conclusions? The authors of this study have shown the importance of considering patient satisfaction, using PROMIS scores, and setting realistic expectations after TAR. The PASS thresholds for TAR are poorer than the population norm, so patients do not need to reach normal pain or physical function levels to have an acceptable symptom state after surgery. The overall PASS achievement rate was 84%. Thus, 1 in 6 patients did not achieve symptoms and function after TAR, similar to the rates in studies of hip and knee arthroplasty.
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 | 0,208 | 0,576 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,004 |
| Bibliométrie | 0,006 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,016 |
| Communication savante | 0,008 | 0,012 |
| Science ouverte | 0,013 | 0,004 |
| Intégrité de la recherche | 0,016 | 0,027 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,003 |
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 ».