Clinical Safety of Pudilan Xiaoyan Oral Liquid for the Treatment of Upper Respiratory Tract Infection in the Real World: Protocol for a Prospective, Observational, Registry Study
Notice bibliographique
Résumé
BACKGROUND: Pudilan Xiaoyan oral liquid (PDL) is a proprietary Chinese medicine preparation widely used for upper respiratory tract infection, known for its significant therapeutic effects. However, the safety profiles reported in several observational studies vary, and these studies primarily focus on efficacy rather than specifically addressing safety concerns, thus representing inadequate safety monitoring. OBJECTIVE: This study aimed to investigate the incidence of adverse drug reactions (ADRs) associated with PDL and explore the factors contributing to these reactions. METHODS: The study is a prospective, observational, multicenter, hospital-based surveillance study. A total of 17 hospitals from China are involved. The study is expected to enroll a large sample of 10,000 patients aged between 18 and 80 years with upper respiratory tract infection who were prescribed PDL. The patients' data, including demographics, medical history, diagnostic information, medication details, adverse events, and laboratory test results, will be monitored. The occurrence of ADRs will be recorded. The primary outcome is the incidence of ADR. Secondary outcomes are the ratio of patients whose body temperature return to the normal range (cases of body temperature normalization and the duration for achieving normal body temperature within a 3-day period will be documented) and changes in liver and kidney function (occurrence of drug-induced liver injury and acute kidney injury). Descriptive analyses will be performed for the primary and secondary outcomes. A cohort, nested, case-control study design will be used. If one patient has an ADR, then 4 patients without ADRs will be matched as the control group according to gender, age within 5 years, drug batch, and other factors, at a ratio of 1∶4 to compare the symptoms related to ADRs. The differences of ADR incidence among the possible influencing factors will be compared separately to find the factors with large differences. Then, synthetic minority oversampling technique and group least absolute shrinkage and selection operator methods will be used to identify factors influencing the occurrence of ADRs. Finally, propensity scoring methods will be used to control for confounding variables. The progress of each subcenter will be closely monitored, and the incidence of ADR will be systematically calculated. Furthermore, the characteristics and influencing factors of ADR will be analyzed, along with an investigation into its geographical distribution. RESULTS: The study began on July 17, 2019. Due to the limited number of eligible patients, missed follow-ups, and the huge clinical burden caused by public health events in 2019, the final case will be enrolled on August 30, 2025. CONCLUSIONS: This study will obtain safety results of PDL in the real world and provide guidance on the clinical safety of traditional Chinese medicine formulations. TRIAL REGISTRATION: ClinicalTrials.gov NCT04031651; https://clinicaltrials.gov/study/NCT04031651. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/65789.
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 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 | 0,041 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,004 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,002 | 0,003 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,004 |
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 ».