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Record W2399545751 · doi:10.3928/1081-597x-20010302-17

Photorefractive Keratectomy With Customized Segmental Ablation to Correct Irregular Astigmatism After Laser in situ Keratomileusis

2001· article· en· W2399545751 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Refractive Surgery · 2001
Typearticle
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsGimbel Eye Centre
Fundersnot available
KeywordsKeratomileusisAblationPhotorefractive keratectomyLASIKAstigmatismMedicineOphthalmologyCorneal topographyVisual acuityExcimer laserLaserOptometryOpticsPhysics

Abstract

fetched live from OpenAlex

PURPOSE: To correct irregular astigmatism after laser in situ keratomileusis (LASIK) with customized segmental ablation using the Nidek OPD-Scan (ARK-10000) to guide the Nidek EC-5000 excimer laser with the Final Fit software. METHODS: One eye of a patient that had undergone LASIK and one enhancement was treated using photorefractive keratectomy (PRK) with customized segmental ablation. OPD-Scan maps were analyzed before and after surgery. The Final Fit software was used to link the OPD-Scan to the EC-5000 laser to guide the customized ablation. RESULTS: At 1 month postoperatively, best spectacle-corrected visual acuity had improved from 20/20-2 to 20/15-2, the patient's subjective evaluation of vision was markedly improved, and the postoperative OPD-Scan maps appeared more regular. CONCLUSION: Customized segmental ablation can be performed using the Nidek OPD-Scan and Final Fit software to improve best spectacle-corrected visual acuity and minimize irregular astigmatism, which may result from prior surgical procedures.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.188
Threshold uncertainty score0.892

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.248
Teacher spread0.239 · 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