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Posterior lamellar keratoplasty: techniques, outcomes, and recent advances

2021· review· en· W3138052737 on OpenAlexaff
Elizabeth Yeu, José Álvaro Pereira Gomes, Brandon D. Ayres, Clara C. Chan, Preeya K. Gupta, Kenneth A. Beckman, Marjan Farid, Edward J. Holland, Terry Kim, Christopher E. Starr, Francis S. Mah

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2021
Typereview
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsNorth Toronto Eye Care
Fundersnot available
KeywordsMedicineVisual rehabilitationCorneal diseaseCorneal transplantationSurgeryOphthalmologyOptometryCorneaVisual acuity

Abstract

fetched live from OpenAlex

Over the past 2 decades, posterior lamellar keratoplasty (PLK) has emerged as an alternative to penetrating keratoplasty in the treatment of corneal endothelial disorders. The reasons for this trend include the search for a safer procedure to replace diseased endothelium that provides faster and better visual rehabilitation and reduces the need for postoperative care. Different surgical techniques, surgical instruments, devices, and lasers have been introduced to overcome technical difficulties, thus improving clinical outcomes. Yet, surgeons and eye banks must address the complications and limitations that arise during the transition to these new techniques. This review discusses the most significant aspects of the evolution of PLK, including a detailed description of current techniques and the direction of future treatment for corneal endothelial disease with the use of laser-assisted surgery, bioengineered corneas, cell therapy, and new pharmacologic therapy.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.041
GPT teacher head0.358
Teacher spread0.318 · 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 designNot applicable
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

Citations8
Published2021
Admission routes1
Has abstractyes

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