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Record W4415474200 · doi:10.4103/joco.joco_247_24

Comparing Lower- and Higher-Order Aberrations: Zywave® II Hartmann–Shack Wavefront Aberrometer versus Peramis Pyramidal Aberrometer

2025· article· en· W4415474200 on OpenAlexaff
Siamak Zarei‐Ghanavati, Mojtaba Abrishami, Maryam Hedayati, Elham Bakhtiari

Bibliographic record

VenueJournal of Current Ophthalmology · 2025
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsWavefrontZernike polynomialsRefractive error

Abstract

fetched live from OpenAlex

Purpose: II and Peramis CSO aberrometers for lower- and higher-order aberration (LOA and HOA) measurements on dynamic conditions without cycloplegia. Methods: In this prospective comparative study, participants aged 20-45 years were examined. Exclusion criteria included previous ocular surgery or trauma, recent contact lens wear, and any ocular or systemic diseases. Each device was operated by an experienced operator who remained blind to the data obtained from the other aberrometer. We compared LOA measurements and the root mean square (RMS) of coma, spherical aberration, and total third- and fourth-order HOAs between the two devices, and the optical zone for measuring HOAs was the same in both the devices. Results: In the study involving 42 eyes of 21 participants (52.4%, female), excellent agreement was observed in LOAs (sphere and cylinder) for both the right and left eyes, with intraclass correlation coefficients of 0.96 and 0.95, respectively, using a 6 mm pupil. In addition, good-to-excellent reliability was reported for the agreement between the two devices in total HOA (t.HOA) and the RMS error of the total aberration, for pupil sizes of 5 mm and 6 mm. However, there was poor agreement between the two devices for third- and fourth-order aberrations in both the pupil sizes. Conclusions: aberrometers in measuring sphere, cylinder, t.HOA, and total aberration. Nevertheless, notable differences were identified in third- and fourth-order aberrations, suggesting that specific measurements may not consistently align between devices, and these values should not be considered interchangeable.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.097
GPT teacher head0.404
Teacher spread0.306 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations1
Published2025
Admission routes1
Has abstractyes

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