An open-label, parallel-group, randomized clinical trial of different silver diamine fluoride application intervals to arrest dental caries
Notice bibliographique
Résumé
BACKGROUND: Silver diamine fluoride (SDF) is an antimicrobial agent and alternative treatment option that can be used to arrest dental decay. While there is optimism with SDF with regard to caries management, there is no true consensus on the number and frequency of applications for children. The purpose of this study was to examine the effectiveness of 38% SDF to arrest early childhood caries (ECC) at three different application regimen intervals. METHODS: Children with teeth that met International Caries Detection and Assessment System codes 5 or 6 criteria were recruited from community dental clinics into an open-label, parallel-group, randomized clinical trial from October 2019 to June 2021. Participants were randomized to one of three groups using sealed envelopes that were prepared with one of three regimens inside: visits one month, four months, or six months apart. Participants received applications of 38% SDF, along with 5% sodium fluoride varnish (NaFV), at the first two visits to treat cavitated carious lesions. Lesions were followed and arrest rates were calculated. Lesions were considered arrested if they were hard on probing and black in colour. Statistics included descriptive and bivariate analyses (Kruskal one-way analysis of variance and Pearson's Chi-squared test). A p-value of ≤ 0.05 was considered significant. RESULTS: Eighty-four children participated in the study (49 males and 35 females, mean age: 44.4 ± 14.2 months). Treatment groups were well matched with 28 participants per group. A total of 374 teeth and 505 lesions were followed. Posterior lesions represented only 40.6% of affected surfaces. Almost all SDF treated lesions were arrested for the one-month (192/196, 98%) and four-month (159/166, 95.8%) interval groups at the final visit. The six-month group experienced the lowest arrest rates; only 72% (103/143) of lesions were arrested (p < 0.001). The duration of application intervals was inversely associated with improvements in arrest rates for all lesions. CONCLUSIONS: Two applications of 38% SDF and 5% NaFV in one-month and four-month intervals were comparable and very effective in arresting ECC. Applications six months apart were less effective and could be considered inferior treatment. TRIAL REGISTRATION: ClinicalTrials.gov NCT04054635 (first registered 13/08/2019).
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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 | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 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 | 0,001 | 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 ».