Validity of the Parsley Symptom Index—an Electronic Patient-Reported Outcomes Measure Designed for Telehealth: Prospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Electronic patient-reported outcomes measures (e-PROMs) are a valuable tool for the monitoring and management of chronic conditions over time. However, there are few validated tools available that capture symptoms across body systems in telehealth settings. The Parsley Symptom Index (PSI) is a recently developed symptom assessment for adults with chronic disease in telehealth settings. A previous study demonstrated the feasibility and acceptability of the PSI in a clinical telehealth setting. OBJECTIVE: The purpose of this study was to assess convergent validity between the PSI and the self-rated health (SRH) item. METHODS: This prospective cohort study took place from January 15, 2021, to December 15, 2021, among a sample of 10,519 adult patients at Parsley Health, a subscription-based holistic medical practice. The PSI and the SRH were completed by patients via an online portal. The association between the PSI and SRH was assessed via polyserial and polychoric correlations, while weighted κ scores provided information related to agreement between the PSI and SRH. RESULTS: From 22,748 responses, there were moderate levels of association (polyserial r=0.51; polychoric r=0.52) and agreement (weighted κ=0.46) between the PSI and SRH. In total, 74.13% (n=16,865) of responses between the PSI and SRH were relatively congruent while 36.17% (n=8229) were literally congruent. CONCLUSIONS: The PSI demonstrates convergent validity with the SRH for adults with chronic disease in a telehealth setting. This finding further supports the validation of the PSI in a real-world clinical setting. Although it is conceptually similar to the 1-question SRH, the PSI is a 45-item PROM designed to capture quality of life and specific symptoms by body system. Future studies will compare the PSI to multi-item PROMs.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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".