Comparison of Humphrey versus Compass Perimetry for Hemianopsia
Bibliographic record
Abstract
Up to 57% of stroke patients experience visual defects. Visual restitution therapy post-stroke remains controversial, with some attributing improvements to adaptive eye scanning movements rather than true field augmentation. We compare Compass fundus-tracking perimetry (CMP), which compensates for eye movements, to the Humphrey Field Analyzer (HFA) for homonymous hemianopsia. Nine patients (mean age: 47) with homonymous hemianopsia on HFA and corresponding neuroimaging defect were prospectively tested on the same day using the HFA (24–2 SITA Fast) and CMP (24–2 ZEST fast). Reliability indices, mean deviation (MD), and visual field index (VFI for HFA; FDPI for Compass) were compared via median differences and the Wilcoxon signed-rank test. The CMP had a significantly lower MD (median difference: −0.33 dB, p = .02), significantly greater false negative rate (median difference: 27%, p = .04), and a significantly longer test duration (median difference: 87 seconds, p = .01) than HFA. However, no between-analyzer difference occurred for visual field index (median difference: 6.8%, p = .65), false positive rate (median difference: −2.8%, p = .18), CMP blind spot index and HFA fixation losses (median difference: 0, p = .79). Bland-Altman plots showed acceptable agreement, with a + 2.42 dB bias in MD favoring CMP. CMP offers real-time compensation for fixation losses but did not show a clinically significant advantage over HFA for homonymous hemianopsia.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".