VALIDATION OF M-CHARTS FOR QUANTITATIVE ASSESSMENT OF METAMORPHOPSIA FOLLOWING RHEGMATOGENOUS RETINAL DETACHMENT REPAIR
Bibliographic record
Abstract
PURPOSE: To validate the quantitative assessment of metamorphopsia in rhegmatogenous retinal detachment (RRD) using M-CHARTS by determining its correlation with subjective reporting of metamorphopsia with a validated metamorphopsia questionnaire (modified MeMoQ). METHODS: The Research Ethics Board approved a prospective observational study carried out at St. Michael's Hospital, Toronto, Canada. Patients with primary, unilateral RRD and healthy controls were included. Metamorphopsia at 3 months was assessed with modified MeMoQ and M-CHARTS. RESULTS: One hundred patients (50 with RRD, 50 controls) were included. Seventy percent (35/50) of the RRD group had metamorphopsia with M-CHARTS and 80% (40/50) with MeMoQ. The modified MeMoQ and total M-CHARTS scores were significantly higher in patients with RRD compared with controls ( P < 0.0001). Cronbach's alpha reliability coefficient was 0.934 in the RRD group. Horizontal, vertical, and total M-CHARTS scores were significantly correlated with MeMoQ scores (r s = 0.465, P = 0.0007; r s = 0.405, P = 0.004; r s = 0.475, P = 0.0005, respectively). M-CHARTS was 72.7% sensitive and 94.6% specific for detection of metamorphopsia (positive score ≥ 0.2), with an area under the receiver operating characteristic curve = 0.801. A stronger correlation was found in patients who scored ≥0.2 on the M-CHARTS and reported metamorphopsia with the MeMoQ (r s = 0.454, P = 0.001). CONCLUSION: The authors have validated M-CHARTS as a tool to quantitatively assess metamorphopsia in patients with RRD, which is significantly correlated with patient-reported outcomes using the MeMoQ. A total score of ≥0.2 with M-CHARTS was more strongly correlated with MeMoQ.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".