A Novel, Integrative Approach for Evaluating Progression in Multiple Sclerosis: Development of a Scoring Algorithm
Notice bibliographique
Résumé
BACKGROUND: There is an unmet need for a tool that helps to evaluate patients who are at risk of progressing from relapsing-remitting multiple sclerosis to secondary progressive multiple sclerosis (SPMS). A new tool supporting the evaluation of early signs suggestive of progression in multiple sclerosis (MS) has been developed. In the initial stage, concepts relevant to progression were identified using a mixed method approach involving regression on data from a real-world observational study and qualitative research with patients and physicians. The tool was drafted in a questionnaire format to assess these variables. OBJECTIVE: This study aimed to develop the scoring algorithm for the tool, using both quantitative and qualitative research methods. METHODS: The draft scoring algorithm was developed using two approaches: quantitative analysis of real-world data and qualitative analysis based on physician interviews and ranking and weighting exercises. Variables that were included in the draft tool and regarded as most clinically relevant were selected for inclusion in a multiple logistic regression. The analyses were run using physician-reported data and patient-reported data. Subsequently, a ranking and weighting exercise was conducted with 8 experienced neurologists as part of semistructured interviews. Physicians were presented with the variables included in the draft tool and were asked to rank them in order of strength of contribution to progression and assign a weight by providing a percentage of the overall contribution. Physicians were also asked to explain their ranking and weighting choices. Concordance between physicians was explored. RESULTS: Multiple logistic regression identified age, MS disease activity, and Expanded Disability Status Scale score as the most significant physician-reported predictors of progression to SPMS. Patient age, mobility, and self-care were identified as the strongest patient-reported predictors of progression to SPMS. In physician interviews, the variables ranked and weighted as most important were stability or worsening of symptoms, intermittent or persistent symptoms, and presence of ambulatory and cognitive symptoms. Across all physicians, the level of concordance was 0.278 (P<.001), indicating a low to moderate, but statistically significant, level of agreement. Variables were categorized as high (n=8), moderate (n=8), or low (n=10) importance based on the findings from the different approaches described above. Accordingly, the respective questions in the tool were assigned a weight of "three," "two," or "one" to inform the draft scoring algorithm. CONCLUSIONS: This study further confirms the need for a tool to provide a consistent, comprehensive approach across physicians to support the early evaluation of signs indicative of progression to SPMS. The novel and comprehensive approach to develop the draft scoring algorithm triangulates data obtained from ranking and weighting exercises, qualitative interviews, and a real-world observational study. Variables that go beyond the clinically most obvious impairment in lower limbs have been identified as relevant subtle/sensitive signs suggestive of progressive disease.
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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 | 0,038 | 0,087 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,007 | 0,005 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,003 | 0,004 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 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 ».