The Development of a Digital Patient-Reported Outcome Measurement for Adults With Chronic Disease (The Parsley Symptom Index): Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: The monitoring and management of chronic illness has always been a challenge. Patient-reported outcome measures (PROMs) can be powerful tools for monitoring symptoms and guiding treatment of chronic diseases, but the available PROM tools are either too broad or too disease specific for the needs of a primary care practice focused on longitudinal care. OBJECTIVE: In this study we describe the development and preliminary validation of the Parsley Symptom Index (PSI). METHODS: This prospective cohort study took place from January 5, 2018, to June 05, 2020, among a sample of 4621 adult patients at Parsley Health. After a review of literature, followed by binning and winnowing of potential items, a 45-item PROM that also served as a review of systems (ROS) was developed. The PSI was deployed and completed by patients via an online portal. Construct and face validity was performed by clinicians, tested on patients, and feasibility was measured by response rate, completion rate, and percentage of missing data. RESULTS: The response rate for 12,175 collected PSIs was 93.72% (4331/4621) with a 100% item completion rate. A confirmatory factor analysis confirmed the model structure was satisfactory by a Comparative Fit Index of 0.943, Tucker-Lewis index of 0.938, and root mean square error of approximation of 0.028. CONCLUSIONS: A 45-item ROS-style PROM designed to capture chronic disease symptoms was developed, and preliminary validation suggests that the PSI can be deployed, completed, and helpful to both patients and clinicians.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".