Predictive Criterion Validity of the Parsley Symptom Index Against the Patient-Reported Outcomes Measurement Information System-10 in a Chronic Disease Cohort: Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: Approximately 60% of US adults live with chronic disease, imposing a significant burden on patients and the health care system. With the rise of telehealth, patient-reported outcomes measures (PROMs) have emerged as pivotal tools for managing chronic disease. While numerous PROMs exist, few have been designed explicitly for telehealth settings. The Parsley Symptom Index (PSI) is an electronic patient-reported outcome measure (ePROM) developed specifically for telehealth environments. OBJECTIVE: Our aim is to determine whether the PSI predicts changes in the established Patient-Reported Outcomes Measurement Information System-10 (PROMIS-10) Global Health, a 10-question short form. METHODS: We conducted a retrospective cohort study using data from 367 unique patients, amassing 1170 observations between August 30, 2017, and January 30, 2023. Patients completed the PSI and the PROMIS-10 multiple times throughout the study period. Using univariate regression models, we assess the predictive criterion validity of the PSI against PROMIS-10 scores. RESULTS: This study revealed significant relationships between the PSI and PROMIS-10 physical and mental health scores through comprehensive univariate analyses, thus establishing support for the criterion validity of the PSI. These analyses highlighted the PSI's potential as an insightful tool for understanding and predicting both mental and physical health dimensions. CONCLUSIONS: Our findings emphasize the importance of the PSI in capturing the nuanced interactions between symptomatology and health outcomes. These insights reinforce the value of the PSI in clinical contexts and support its potential as a versatile tool in both research and practice.
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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 | 0.008 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".