Validation of an Individualized Measure of Quality of Life, Patient Generated Index, for Use with People with Parkinson’s Disease
Bibliographic record
Abstract
Introduction. Parkinson’s disease (PD) affects all aspects of an individual’s life and is heterogeneous across people and time. The Patient Generated Index (PGI) is an individualized measure of quality of life (QOL) that allows patients to identify the areas of life that are important to them. Although the PGI has immense potential for use in clinical and research settings, its validity has not been assessed in PD. The purpose of this study is to estimate how well areas of QOL that patients with PD nominate on the PGI agree with ratings obtained from standard outcome measures. Methods. Patients with PD completed the PGI and various standard patient-reported outcome (PRO) measures. The PGI and standard PRO measures were compared at the total score, domain, and item levels. Pearson’s correlations and independent t-tests were used, as well as positive and negative predictive values. Results. The sample (n = 76) had a mean age of 69 (standard deviation 9) and were predominantly men (59%). The PGI was moderately correlated (r = −0.35) with the standardized disease-specific QOL measure Parkinson’s Disease Questionnaire (PDQ-8). Within one severity rating, agreement between the PGI and different standard outcome measures ranged from 85 to 100% for walking, 69 to 100% for fatigue, 38 to 75% for depression, and 20 to 80% for memory/concentration. Conclusion. This study demonstrates that nominated areas of QOL on the PGI provide comparable results to standard PRO measures, and provides evidence in support of the validity of this individualized measure in PD.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".