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Record W2782384459 · doi:10.1371/journal.pone.0190621

Optical coherence tomography for glaucoma diagnosis: An evidence based meta-analysis

2018· review· en· W2782384459 on OpenAlexaff
Vinay Kansal, James J. Armstrong, Robert Pintwala, Cindy Hutnik

Bibliographic record

VenuePLoS ONE · 2018
Typereview
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsSt. Joseph's HospitalWestern UniversityUniversity of Saskatchewan
Fundersnot available
KeywordsGlaucomaMedicineOphthalmologyOptical coherence tomographyReceiver operating characteristicGanglionCochrane LibraryOptometryMeta-analysisPathologyAnatomyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: Early detection, monitoring and understanding of changes in the retina are central to the diagnosis of glaucomatous optic neuropathy, and vital to reduce visual loss from this progressive condition. The main objective of this investigation was to compare glaucoma diagnostic accuracy of commercially available optical coherence tomography (OCT) devices (Zeiss Stratus, Zeiss Cirrus, Heidelberg Spectralis and Optovue RTVue, and Topcon 3D-OCT). PATIENTS: 16,104 glaucomatous and 11,543 normal eyes reported in 150 studies. METHODS: Between Jan. 2017 and Feb 2017, MEDLINE®, EMBASE®, CINAHL®, Cochrane Library®, Web of Science®, and BIOSIS® were searched for studies assessing glaucoma diagnostic accuracy of the aforementioned OCT devices. Meta-analysis was performed pooling area under the receiver operating characteristic curve (AUROC) estimates for all devices, stratified by OCT type (RNFL, macula), and area imaged. RESULTS: 150 studies with 16,104 glaucomatous and 11,543 normal control eyes were included. Key findings: AUROC of glaucoma diagnosis for RNFL average for all glaucoma patients was 0.897 (0.887-0.906, n = 16,782 patient eyes), for macula ganglion cell complex (GCC) was 0.885 (0.869-0.901, n = 4841 eyes), for macula ganglion cell inner plexiform layer (GCIPL) was 0.858 (0.835-0.880, n = 4211 eyes), and for total macular thickness was 0.795 (0.754-0.834, n = 1063 eyes). CONCLUSION: The classification capability was similar across all 5 OCT devices. More diagnostically favorable AUROCs were demonstrated in patients with increased glaucoma severity. Diagnostic accuracy of RNFL and segmented macular regions (GCIPL, GCC) scans were similar and higher than total macular thickness. This study provides a synthesis of contemporary evidence with features of robust inclusion criteria and large sample size. These findings may provide guidance to clinicians when navigating this rapidly evolving diagnostic area characterized by numerous options.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.030
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0140.041
Bibliometrics0.0060.007
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0020.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.300
GPT teacher head0.368
Teacher spread0.068 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations102
Published2018
Admission routes1
Has abstractyes

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