Visualizing the Consistency of Clinical Characteristics that Distinguish Healthy Persons, Glaucoma Suspect Patients, and Manifest Glaucoma Patients
Bibliographic record
Abstract
PURPOSE: To use factor analysis to visualize and assess the reproducibility and consistency of clinical quantitative parameters that can optimally distinguish among healthy, glaucoma suspect, and manifest glaucoma patients at a cross-sectional level and thus to describe the transition of quantitative change among the diagnostic categories. DESIGN: Retrospective cross-sectional study. PARTICIPANTS: The medical records of healthy, glaucoma suspect, and manifest glaucoma patients (diagnosed by expert clinicians) seen at the Centre for Eye Health in 2015 (n = 148, n = 664, and n = 129, respectively) and 2018 (n = 242, n = 464, and n = 126, respectively) were reviewed. One eye was selected for the study. METHODS: Quantitative clinical measures (intraocular pressure [IOP], central corneal thickness [CCT], visual field [VF], and OCT) were extracted and binary logistic (backward stepwise) regression was performed to identify factors that dictated separation between diagnostic pairs. These were used systematically as inputs for factor analysis to determine a final model that could potentially predict a clinical diagnosis. MAIN OUTCOME MEASURES: Intraocular pressure, CCT, VF (mean deviation and pattern standard deviation) indices, and OCT optic nerve head parameters and thickness values (retinal nerve fiber layer [RNFL] and ganglion cell-inner plexiform layer). RESULTS: Few clinical parameters were identified commonly as significant across all diagnostic pairings for 2015 (3 of 23: IOP, pattern standard deviation, and 7-o'clock RNFL thickness) and 2018 (1 of 23: vertical cup-to-disc ratio). Few parameters overlapped when comparing 2015 and 2018 results, highlighting inconsistencies in the models between years. Factor analysis showed good separation between healthy persons and glaucoma patients. Using biplots to visualize the data in 2-dimensional clusters, glaucoma suspect patients demonstrated substantial overlap with healthy and glaucoma cohorts. The contributions of each parameter to diagnostic separation changed between groups and years. CONCLUSIONS: Despite advances in quantitative ocular imaging and perimetry, the transition among healthy, glaucoma suspect, and manifest glaucoma patients remains confounded by a lack of consistent, reproducible combinations of quantitative clinical criteria. These results highlight the nebulousness (at patient-, instrument-, and clinician-related levels) of glaucoma diagnosis that remains contingent on individual clinical expertise and assessment.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".