Predicting the outcome of AIS brace treatment using expert judgement and a fuzzy model
Notice bibliographique
Résumé
This work measured how accurately the outcome of brace treatment can be predicted, using only information available at the start of treatment. The prediction accuracies of human experts and a fuzzy computer model were measured and compared. Data was obtained retrospectively from 28 AIS patients who had finished treatment (27 girls, 1 boy, aged 11-15 (mean 13), Cobb angles 20-44 degrees (mean 31), 21 daytime and 7 nighttime braces.) Patients were labelled 'progressed' if their Cobb angle had increased more than 5 degrees by the end of treatment, and 'non-progressed' otherwise. A fuzzy model was developed to predict treatment outcome for each patient using clinical measurements taken at the first in-brace clinic. The model considers patient age, Cobb angle, Scoliometer measurement at the apex level, and in-brace Cobb angle correction. For each patient it calculated a probability-like score for each of three possible outcomes: 'progression' (Cobb angle increase > 5 degrees), 'neutral' (Cobb angle change of 0-5 degrees), and 'improvement' (Cobb angle decrease). For this study, the patient was predicted to progress if the 'progression' score was the highest. Five AIS experts also participated: two orthopaedic surgeons, two orthotists, and one nurse practitioner. Participants were supplied with all available start-of-treatment clinical measurements for each patient, and asked to predict whether or not each patient would progress by the end of brace treatment. The multi-rater kappa was calculated to measure agreement between experts' predictions. The correlation of each expert's predictions and the model's predictions with the actual treatment outcome was measured. Correlation between the fuzzy model's predictions and the actual outcomes was 0.7. Correlations between the five expert's predictions and the actual outcomes were 0.52, 0.52, 0.58, 0.46, and 0.71. The agreement among the human experts' predictions was k=0.43. Twelve of the twenty-eight patients had actually progressed; experts predicted 12-16 progressions, while the model predicted 17. Brace treatment outcome can be predicted at the start of treatment with moderate accuracy. The fuzzy model predicted treatment outcome more accurately than four human experts, and was comparable to the fifth. This type of fuzzy model works by estimating patient-specific likelihoods of several possible outcomes; future use of such models as decision-support aids may help inform clinical decision-making.
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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 | 0,000 |
| 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 ».