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Record W2070100803 · doi:10.1167/iovs.06-0579

Automated Analysis of Heidelberg Retina Tomograph Optic Disc Images by Glaucoma Probability Score

2006· article· en· W2070100803 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueInvestigative Ophthalmology & Visual Science · 2006
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsnot available
FundersHeidelberg EngineeringUniversity of ManchesterNova Scotia Health Research Foundation
KeywordsGlaucomaOptic discMedicineOphthalmologyReceiver operating characteristicAbsolute deviationLogistic regressionOptometryNuclear medicineMathematicsStatisticsInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To compare the diagnostic performance of the Heidelberg Retinal Tomograph's (HRT; Heidelberg Engineering GmbH, Dossenheim, Germany) glaucoma probability score (GPS), an automated, contour line-independent method of optic disc analysis with that of the Moorfields regression analysis (MRA). METHODS: HRT images were obtained from one eye of 121 patients with glaucoma (median age, 70.2 years; median mean deviation [MD], -3.6 dB, range, +2.0 to -9.9 dB) and 95 healthy control subjects (median age, 59.7 years; median MD -0.1 dB, range +2.5 to -3.7). The diagnostic performances of GPS and MRA were evaluated by including borderline classifications, either as test negatives (most specific criteria) or as test positives (least specific criteria). Agreement between global and sectoral data of both analyses was established. Logistic regression analyses were performed to evaluate the effect of covariates such as optic disc size and age on the classification outcomes of both the GPS and the MRA. RESULTS: In 8 (7%) patients with glaucoma and 10 (11%) control subjects, the GPS failed to provide a complete global and sectoral optic disc classification. Although we could not identify a single distinct cause of this failure in the glaucoma group, failures in the control subjects occurred most often (7/10) with small and crowded optic discs. In subjects who were successfully classified at least globally by the GPS (117 patients with glaucoma, 88 control subjects), the diagnostic performances of GPS and MRA were similar (areas under the receiver operating characteristic [ROC] curve of 0.78 and 0.77, respectively; P > 0.1). With the GPS, sensitivity and specificity were 59% and 91% (most specific criteria) and 78% and 63% (least specific criteria), respectively. Combining GPS and MRA did not increase diagnostic performance significantly (ROC area of combined classifiers, 0.81). Both GPS and MRA were affected by disc size. In patients with glaucoma as well as healthy control subjects, the odds of a positive GPS classification (borderline or outside normal limits) increased by 21% (95% confidence interval [CI], 12%-30%) for each 0.1 mm2 increase in optic disc area. With the MRA, the corresponding increase was 15% (95% CI, 7%-23%). Optic disc area alone accounted for approximately 30% and 22% of the explained variance with the GPS and MRA, respectively (P < 0.001). The proportional-odds logistic regression confirmed that optic disc size affected mainly the tradeoff between true- and false-positive classifications (criterion) rather than the absolute performance of the analyses (area under the ROC curve). There was some evidence of an age effect with the MRA, which showed a 53% (95% CI, 16%-102%) increase in the odds of a positive test (borderline or outside normal limits) associated with each decade of age (P = 0.002), but no age effects were observed with the GPS (P > 0.1). CONCLUSIONS: The diagnostic performance of the contour line-independent GPS analysis is similar to that of the MRA. However, clinicians should be aware of the strong size dependence of both GPS and MRA. In large optic discs, both GPS and MRA are likely to produce many false-positive classifications. Correspondingly, the sensitivity to early damage is likely to be low in small optic discs. There is a need for automated classification systems that explicitly address the size dependence of current analyses.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.256
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.006
Science and technology studies0.0000.012
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.018
GPT teacher head0.313
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it