Comment on “Early Efficacy of Intra-Articular HYADD® 4 (Hymovis®) Injections for Symptomatic Knee Osteoarthritis”
Notice bibliographique
Résumé
We read with great interest the study by Priano titled “Early efficacy of intra-articular HYADD® 4 (Hymovis®) injections for symptomatic knee osteoarthritis.”[ 1 ] The author would like to explore the efficacy of intra-articular HYADD 4 (Hymovis) injections for symptomatic knee osteoarthritis. Results from this study are very interesting and promising from a clinical aspect; however, we believe that studying patient's clinical status with visual analog scale and Western Ontario and McMaster Universities Arthritis Index scale should be supported by biomechanical information. From this point of view, to have more data that could influence the clinical practice, it is important to note the possible action that intra-articular injections of different kinds of hyaluronic acid could have on walking biomechanics using an objective measurement tool as gait analysis. In our opinion, the work by Priano[ 1 ] is promising because it investigates the efficacy of a new formulation of hyaluronic acid. Nowadays, many hyaluronic acid formulations are approved for clinical use in Europe and the United States. Furthermore, hyaluronic acid injections' efficacy has been demonstrated also in hip osteoarthritis.[ 2 ] However, even if these formulations differ in their chemical–physical properties, joint space half-life, rheological properties, and clinical efficacy, there are few studies that investigate hyaluronic acid's possible action from a biomechanical point of view.[ 3 ] [ 4 ] From this point of view, we believe that osteoarthritis management and rehabilitation should be prescribed after an objective analysis of functional walking alterations using gait analysis instrumentations. The use of gait analysis should be desirable during diagnosis and follow-up. In fact, it is capable to identify different walking patterns in patient with osteoarthritis of the lower limbs, whereas the radiology can evaluate the status of the joint's structures. Moreover, gait analysis can find the exact altered phase of the walking cycle, guaranteeing a precise prescription of a rehabilitation program, giving the clinician data about spatial–temporal parameters, kinematic and kinetic alterations, and about the surface electrical muscle activity using surface electromyography. In conclusion, gait analysis is easily applicable to most of the patients, without side effects. Hence, this instrumentation is suitable for follow-up evaluations and permits to assess any variations of walking biomechanics over time. As an example, we would like to present a case of a patient (female, 42 years old) affected by knee osteoarthritis (II grade—Kellgren and Lawrence classification), treated with two intra-articular injection of mobile reticulum hyaluronic acid, evaluated with gait analysis before treatment and 6 months after the treatment. In our patients is evident the improvement of the flexion–extension kinematic as a consequence of the treatment performed, in particular, we would like to underline the amelioration of the first flexion peak at the loading response phase of the walking cycle ([ Fig. 1 ]). Finally, we would like to underline how gait analysis could represent very important outcome measurement to determinate the efficacy of intra-articular injection therapy to treat knee osteoarthritis. Fig. 1 Knee flexion–extension kinematic.
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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 | 0,005 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,005 | 0,001 |
| Intégrité de la recherche | 0,041 | 0,037 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,008 |
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 ».