Cohort profile: OpenPROMPT
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.145 | 0.032 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".