Cohort profile: OpenPROMPT
Notice bibliographique
Résumé
Abstract OpenPROMPT is a cohort of individuals with longitudinal patient reported questionnaire data and linked to routinely collected health data from primary and secondary care. Data were collected between November 2022 and October 2023 in England. OpenPROMPT was designed to measure the impact of long COVID on health-related quality-of-life (HRQoL). With the approval of NHS England we collected responses from 7,574 individuals, with detailed questionnaire responses from 6,337 individuals who responded using a smartphone app. Data were collected from each participant over 90 days at 30-day intervals using questionnaires to ask about HRQoL, productivity and symptoms of long COVID. Responses from the majority of OpenPROMPT (6,006; 79.3%) were linked to participants’ existing health records from primary care, secondary care, COVID-19 testing and vaccination data. Analysis takes place using the OpenSAFELY data analysis platform which provides a secure software interface allowing the analysis of pseudonymized primary care patient records from England. OpenPROMPT can currently be used to estimate the impact of long COVID on HRQoL, and because of the linkage within OpenSAFELY, the data from OpenPROMPT can be used to enrich routinely collected records in further research by approved researchers on behalf of NHS England. Lay summary OpenPROMPT is a study which used a phone app to conduct a longitudinal survey aimed at measuring the health related quality of life of people living with long COVID. The study recruited participants between November 2022 and July 2023 and followed them up for 90 days. The key advantage of this study is that the responses are linked to the individual’s personal health records, so we have access to much more data than the questionnaire responses alone. Here, we summarised who has used the app, how much data has been collected and the quality of the data. We also provide details to document how and why the data were collected so that the data can be used by other researchers in the future. This will maximise the benefit of this study, and ensure that the time invested by participants is put to best use. In this study we aimed to provide lots of important information about how many people are involved, how much information we have about them, their age, where they live, and how healthy they are. Finally, for certain variables we compared the responses people recorded in the app with what is kept on their electronic record to see if they agree or disagree. Key features OpenPROMPT is a cohort of individuals with longitudinal patient reported questionnaire data and linked to routinely collected health data from primary and secondary care. With the approval of NHS England we collected responses from 7,574 individuals, with detailed questionnaire responses from 6,337 individuals who responded using a smartphone app. Data were collected from each participant over 90 days at 30-day intervals using questionnaires to ask about HRQoL, productivity and symptoms of long COVID. Responses from the majority of OpenPROMPT (6,006; 79.3%) were linked to participants’ existing health records from primary care, secondary care, COVID-19 testing and vaccination data. OpenPROMPT can currently be used to estimate the impact of long COVID on HRQoL, and because of the linkage within OpenSAFELY, the data from OpenPROMPT can be used to enrich routinely collected records in further research by approved researchers on behalf of NHS England.
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 | 0,003 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,145 | 0,032 |
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 ».