Data Completeness and Concordance in the FeverApp Registry: Comparative Study
Notice bibliographique
Résumé
BACKGROUND: The FeverApp registry uses ecological momentary assessment (EMA) to collect parental data on pediatric fever for scientific research. The mobile app FeverApp educates parents on safe fever management and serves as a fever diary. OBJECTIVE: The focus of this study was to evaluate the completeness and concordance of the EMA-based FeverApp registry with regard to its data quality from a multilevel perspective. METHODS: Structured descriptions of fever episodes by health care professionals from an office were used as reference. The number of children, their sociodemographic data, and agreement of fever episodes, with maximum temperature, intake of antipyretics and antibiotics, and physician visits, were compared with the entries in the corresponding physician's reference records. The data quality indicators for completeness, meaning the extent to which the necessary data for the registry has actually been submitted, and concordance, which is the correspondence of the value of a data element with a reference source, were chosen to analyze whether EMA may be a suitable method for this kind of registry. RESULTS: In both data sources, 1012 children were available for comparison over 16 months. The completeness of gender (1012/1012, 100%) and date of birth (1004/1012, 99.2%) information was high, and the mismatches were 0.69% (7/1012) and 1.19% (12/1012), respectively, between the sources. Of these 1012 children, 668 (66%) registered fever episodes in FeverApp. They relate to 534 families with 953 fever episodes in the reference records and 1452 episodes in the FeverApp registry. Of the 534 families, 183 (34.3%) refrained from visiting the office during fever episodes but nevertheless documented them in FeverApp. Largest part (766/1452, 52.75%) episodes were recorded exclusively in the FeverApp registry by 371 (371/534, 69.5%) families. The remaining 686 (47.2%) episodes of 391 (58.5%) children from 351 (65.7%) families were comparable with the reference data source in terms of physician visits, medication, and temperature. The completeness ranged, depending on the kind of variable, from 11.5% to 65% in the registry and from 7.6% to 42.6% in the office. The 953 fever episodes reported by the reference office consisted of 681 (71.5%) acute and 272 (28.5%) past episodes. In FeverApp, most past (262/272, 96.3%) but less acute (424/681, 62.3%) episodes have been entered. The concordance rates were varied: 90.2% for antibiotic use, 66.6% for antipyretic use, 61.7% for physician visits, and 16% for the highest temperature during the fever episode. CONCLUSIONS: Both sources delivered only partial data, and the rates of completeness and concordance depended on the kind of variable. However, the FeverApp registry showed higher documentation and precision rates than professional records for all considered variables. Therefore, EMA may play a unique supplement for research in ambulatory care. FeverApp could support pediatric offices, especially during the pandemic.
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 | 0,001 | 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 | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| 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 ».