Comment on “Early Efficacy of Intra-Articular HYADD® 4 (Hymovis®) Injections for Symptomatic Knee Osteoarthritis”
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.041 | 0.037 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".