Comparison of Matrix with Humphrey Field Analyzer II with SITA
Bibliographic record
Abstract
PURPOSE: To study the performance of the Matrix perimeter compared with the Humphrey Field Analyzer II (HFA) with the Swedish Interactive Thresholding Algorithm over the range of contrast sensitivities each machine could estimate. METHODS: Fifty stable glaucoma subjects at various stages of disease and three normal subjects had visual fields testing done on five different days within 8 weeks with both perimeters. Intraclass correlation coefficient of mean deviation, pattern standard deviation, and the SD of repeat measurements were evaluated. The repeatability of the sensitivity estimates at individual locations and global indices was quantified, as well as their dependence on disease severity. The relationship between sensitivity determinations with the two instruments was explored (principal curve analysis). RESULTS: Mean deviation on the HFA ranged from -31 to +2.5 dB. The mean deviation and pattern standard deviation had intraclass correlation coefficients above 0.90 for both instruments. Over most of the useful range (above 20 dB on the HFA), a difference of 1 dB for the Matrix corresponded to a difference of 2 dB for the HFA. The SD of repeat measurements increased with disease severity with HFA, but not with Matrix, except that values of 12 or 34 dB were highly variable on repeat. Variability was reduced for both HFA and Matrix when duplicate sensitivity values were used. A single Matrix test provided only 15 possible sensitivity values, unevenly spaced, but the average of duplicate measurements provided more numerous sensitivity values. A learning effect was detected for Matrix. CONCLUSIONS: The decibel values reported by the two machines are not equivalent. Variability of sensitivity determinations is affected more by the sensitivity level with HFA than with Matrix. Duplicate measurements for baseline and follow-up evaluation could be important, especially for Matrix. Further information on learning effects is needed, as is commercially available progression software for Matrix.
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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 | 0.003 | 0.012 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".