The Coags Uncomplicated App: Fulfilling Educational Gaps Around Diagnosis and Laboratory Testing of Coagulation Disorders
Notice bibliographique
Résumé
BACKGROUND: Patients with coagulation disorders may present to a variety of physician specialties; however, accurate and efficient diagnosis can be challenging for physicians not specialized in hematology, due to identified gaps in knowledge around appropriate laboratory assays and interpretation of test results. Coags Uncomplicated was developed to fill this unmet educational need by increasing practical knowledge of coagulation disorders among nonexpert physicians and other health care professionals (HCPs) in a point-of-care (POC) setting. OBJECTIVE: The aim of this study was to assess patterns of use of the mobile app Coags Uncomplicated, a tool designed to support education regarding accurate and efficient diagnosis of bleeding disorders. METHODS: App metrics were obtained by tracking registered user data. Additionally, a survey was distributed to registered users, to assess circumstances and frequency of use. RESULTS: The most common specialties of 7596 registered US users were hematology-oncology (n=1534, 20.19%), hematology (n=1014, 13.35%), and emergency medicine (n=1222, 16.09%); most identified as physicians (n=4082, 53.74%). Specialties accounting for the greatest numbers of screen views were hematology-oncology (99,390 views), hematology (47,808 views), emergency medicine (23,121 views), and internal medicine (22,586 views). The most common diagnostic endpoints reached were disseminated intravascular coagulation (DIC; 2713 times), liver disease effect (2108 times), and vitamin K deficiency (1584 times). Of 3424 users asked to take the survey, 262 responded (7.65%); most were physicians in direct clinical care (71%) and specialized in hematology-oncology (39%) or emergency medicine (21%). Most frequent use was reported by hematologists (69%, ≥6 times) and hematologists-oncologists (38%, ≥6 times). Most physicians (89.2%) reported using the app for patient-case-related education around appropriate use of laboratory tests in diagnostic evaluation. Physicians rated Lab Value Analyzer (mean 4.43) and Lab Test Algorithm (mean 4.46) tools highly on a 5-point "how helpful" scale and were likely to recommend the app to colleagues. CONCLUSIONS: App use among physicians and other HCPs is consistent with value as a POC educational tool, which may facilitate differential diagnoses and appropriate early consultation with hematologists.
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 | 0,000 | 0,004 |
| 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 | 0,000 |
| 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 ».