Estimating patient‐reported outcomes for glaucoma management: A cross‐sectional study
Bibliographic record
Abstract
AIM: To identify important explanatory variables of four patient-reported outcomes (PROs): vision-related quality of life (VRQoL), preference-based health-related quality of life (HRQoL), social support and community integration and depressive symptoms. METHODS: Cross-sectional study conducted at one ophthalmic practice in a hospital setting. Patients with a diagnosis of glaucoma or glaucoma suspect (n = 250) were sequentially recruited. Patients with language restrictions were excluded. Data were collected through medical chart reviews and face-to-face interviews. The PROs were measured using validated tools. Candidate models for predicting PROs from explanatory variables were constructed using linear and logistic regression, as well as classification and regression trees. Through leave-one-out cross-validation, the performance of each model was assessed in terms of mean absolute error. RESULTS: Use of mobility aids, best corrected visual acuity (BCVA), income, and living arrangements were most predictive of VRQoL, social support, and community integration. Use of mobility aids was also most predictive of the presence of depressive symptoms, and BCVA with preference-based HRQoL. CONCLUSION: Although promising associations were discovered, the models based on commonly collected clinical variables had limited ability to accurately predict individual patient PROs. Thus, although this study identifies clinical and demographic variables that are most predictive of PROs, routine collection of PROs in clinical practice may be necessary to obtain a complete picture of the quality of life of glaucoma patients.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".