Notice bibliographique
Résumé
[Editor's Note: Both the letter to the editor by Guccione and Mielenz and the response by Riddle and colleagues are commenting on the accepted but unedited author manuscript version of this article that was published ahead of print on February 7, 2013.] We appreciate the opportunity to respond to the letter by Guccione and Mielenz.1 Ours was a straightforward replication study of the work by Guccione and colleagues,2 with some additional analyses.3 We studied 1,030 outpatients with isolated musculoskeletal disorders, the same types of disorders as 77% of the 391 outpatients in the study by Guccione et al. The Outpatient Physical Therapy Improvement in Movement Assessment Log (OPTIMAL) is an American Physical Therapy Association–endorsed4 patient-reported outcome instrument that has undergone surprisingly little study since it was first introduced in 2003. Given the importance of replication in determining the extent to which findings reported in one study may generalize to other sites and patients, we thought the psychometric characteristics of OPTIMAL needed re-examination. We found that several of the measurement properties of OPTIMAL were disappointingly low and recommended that clinicians treating outpatients with musculoskeletal disorders consider other instruments that are more psychometrically sound. We focus our comments on issues identified by Guccione and Mielenz that relate to the interpretation of our data. One of our key findings was that patients' responses to the OPTIMAL Difficulty and OPTIMAL Confidence Scales demonstrated extensive overlap. That is, our factor analysis loadings grouped on anatomical site and not separately on difficulty and confidence constructs. In addition, the Pearson r association between the 2 scale scores was .89. Our data suggested that clinicians who use both OPTIMAL scales are not actually measuring difficulty and confidence constructs but rather are capturing essentially the same information with both scores. Guccione and Mielenz discussed findings from unpublished work that they claimed supported use of the OPTIMAL Difficulty and Confidence Scales. Unpublished data, in our view, should not be used to advocate for the utility of an instrument. Guccione and Mielenz questioned the use of region-specific scales as comparators for our convergent construct validity analysis. They contend that the region-specific scales we used have content and theoretical underpinnings that are substantially different from OPTIMAL. As we discussed in our article, the region-specific scales we selected are among the most studied and validated patient-report functional status measures available for patients with musculoskeletal disorders. The purpose of construct validation is to assess the extent to which measures with similar theoretical foundations are associated with one another.5 We agree that the region-specific scales do not capture the same phenomena as OPTIMAL. If they did, there would be no need for OPTIMAL. In our view, the overlap between OPTIMAL items and the region-specific scale items, as well as their conceptual foundations, is considerable and justifies a convergent construct validation approach. We are intrigued by OPTIMAL and see potential advantages to measuring the extent of limitations in the types of items included in the instrument. Unfortunately, our data did not support clinical application for outpatients with musculoskeletal disorders. We look forward to future publications designed to determine the extent to which valid inferences can be drawn from OPTIMAL scores.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| 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 | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
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 tête enseignante, 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 ».