Evaluation of three screening tests and a risk assessment model for diagnosing peripheral neuropathy in the diabetes clinic
Notice bibliographique
Résumé
OBJECTIVE: With the aim of evaluating predictive power, three simple screening tests as alternates to nerve conduction tests for diagnosing diabetic peripheral neuropathy (DPN) were investigated. Results of the screening tests, along with the subjects' demographic and clinical characteristics, were planned as the variables for the development of a risk assessment tool for predicting DPN. DESIGN: This is a cross‐sectional multi‐group comparison study. The study utilized a predictive model derived from one subset of the study population, and prospectively tested in the other subset to predict the presence of neuropathy. SETTING: Diabetic Neuropathy Research Clinic of the Toronto General Hospital and University Health Network in Toronto, Ontario, Canada from June 1998 to August 1999. Sample population: data come from 478 subjects consisting of non‐diabetic reference subjects, and patients with type I and type 2 diabetes mellitus. OUTCOME MEASURES: Nerve conduction studies (NCS) comprised the primary defined outcome. The three screening sensory tests examined in the study were the Semmes‐Weinstein 10 g monofilament examination (SWME), superficial pain sensation, and vibration by the on‐off method. RESULTS: The three screening tests are significantly and positively correlated with NCS. An increase in the number of insensate responses in the screening test is associated with an increase in the abnormal NCS score. The strength of the association between NCS and each sensory test was greater when the neuropathy severity stage of the subject was added to the model. Both the SWME and vibration by the on‐off method tests demonstrated sufficient statistical power to differentiate non‐diabetic control subjects from subjects with diabetes, as well as to differentiate subjects with diabetes with and without neuropathy. These two tests, when compared with NCS, also demonstrated acceptable diagnostic performance characteristics in terms of high sensitivity and specificity, total number of correctly predicted cases, and receiver‐operating characteristic curves. CONCLUSION: This data, through the development of a model involving training and validation sets, demonstrates that the knowledge of clinical risk factors alters the interpretation of sensory tests for DPN. This finding lends further support to the validity of simple sensory testing maneuvers in the conditional diagnosis of DPN. We recommend annual screening with either the SWME or vibration by the on‐off method in the primary care and diabetes clinics.
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 | 0,003 | 0,001 |
| 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 | 0,000 |
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 ».