Calibrating the Impact of Vision Impairment (IVI): Creation of a Sample-Independent Visual Function Measure for Patient-Centered Outcomes Research
Bibliographic record
Abstract
Purpose: Provide item calibrations estimated for the Impact of Vision Impairment (IVI) questionnaire by pooling data from several studies of people with vision impairment (VI) representing a variety of countries and causes of VI. Methods: Eight data sets from six principal investigators representing responses to IVI items from 2867 VI patients were pooled for analysis. Eligible patients were 18 years or older and from Australia, India, and the United States. Rasch analysis, using the Andrich Rating Scale Model (Winsteps version 3.65), was performed on preintervention IVI responses to estimate item and person measures, reliability coefficients, and response category thresholds. Differential item functioning (DIF) analysis and analysis of variance (ANOVA) were used to examine the effects different data sets and covariates on item estimates. Results: Patient age range was 18 to 103 years (median 62 years); 55% were male. Visual acuity ranged from 20/20 to no light perception and primary diagnosis was macular degeneration in 29% of patients. Item measure estimates showed good separation reliability (R2 = 0.99). DIF magnitude did not preclude use of all IVI-28 data. ANOVA showed VA (P < 0.001) and gender (P < 0.002) were predictors of visual ability. Conclusions: Analysis from pooled data support the provision of calibrated IVI item measures for researchers and clinicians to use, thus better enabling direct comparisons of patients with VI. Translational Relevance: Validity testing of the IVI show that we can combine disparate data sets of patient responses to calibrate item measures and response category thresholds, and provide to others for use in comparing patients across clinical trials and on an individual basis.
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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 | 0.042 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".