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Record W2789751025 · doi:10.1097/ico.0000000000001564

Outcomes of Descemet Membrane Endothelial Keratoplasty in Eyes With a Previous Descemet Stripping Automated Endothelial Keratoplasty Graft

2018· article· en· W2789751025 on OpenAlexaff
Nir Sorkin, Mahmood Showail, Adi Einan‐Lifshitz, Tanguy Boutin, Armand Borovik, Mohammad Kreimei, Amir Rosenblatt, Clara C. Chan, David S. Rootman

Bibliographic record

VenueCornea · 2018
Typearticle
Languageen
FieldMedicine
TopicCorneal surgery and disorders
Canadian institutionsToronto Western HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineDescemet membraneVisual acuityOphthalmologyRetrospective cohort studySurgery

Abstract

fetched live from OpenAlex

PURPOSE: To analyze the outcomes of Descemet membrane endothelial keratoplasty (DMEK) in eyes with previous Descemet stripping automated endothelial keratoplasty (DSAEK). METHODS: This retrospective interventional case series included 26 eyes (26 patients) that underwent DMEK to replace a previous DSAEK graft with at least 6 months of follow-up. The outcome measures were indications for surgery, best spectacle-corrected visual acuity (BSCVA), endothelial cell density, rebubbling rate, rejection, and failure. RESULTS: Patient age was 71.9 ± 12.6 years. The average follow-up time after DMEK was 15.1 ± 10.6 months. Indications for DMEK were DSAEK graft failure (69%) and a DSAEK suboptimal visual outcome (31%). BSCVA of the entire cohort (n = 26) improved from 1.33 ± 0.78 logMAR preoperatively to 1.04 ± 0.78 and 1.06 ± 0.89 logMAR at 6 months postoperatively and at the last follow-up, respectively (P = 0.019 and P = 0.033). BSCVA among eyes without visual comorbidities (n = 13) improved from 0.84 ± 0.50 logMAR preoperatively to 0.55 ± 0.47 and 0.51 ± 0.49 logMAR at 6 months postoperatively and at final follow-up, respectively (P = 0.023 for both). Of these eyes, 84.6% had improvement in BSCVA at 6 months postoperatively and at last follow-up. In the subgroup of 8 eyes with DMEK for suboptimal visual outcomes after DSAEK, BSCVA improved from 0.81 ± 0.44 to 0.52 ± 0.35 logMAR at final follow-up (P = 0.024). When excluding eyes with visual comorbidities, BSCVA of this subgroup (n = 5) improved from 0.54 ± 0.32 to 0.36 ± 0.25 logMAR at final follow-up (P = 0.038). BSCVA of this subgroup at 6 months postoperatively was not significantly different from preoperative BSCVA, when including visual comorbidities (n = 8, 0.75 ± 0.60 logMAR, P = 0.79) and when excluding visual comorbidities (n = 5, 0.40 ± 0.28 logMAR, P = 0.621). Endothelial cell density decreased from 2753 ± 307 cells/mm to 1659 ± 655 cells/mm 6 months after surgery (39.7% loss, P = 0.005). Three eyes (11.5%) required rebubbling, and 5 eyes (19.2%) had secondary graft failure at 2 to 20 months. CONCLUSIONS: DMEK is effective for replacing previous DSAEK not only for graft failure but also for suboptimal visual outcomes.

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 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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.226
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.018
GPT teacher head0.267
Teacher spread0.249 · 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 teacher head, not a consensus.

Study designObservational
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".

Quick stats

Citations10
Published2018
Admission routes1
Has abstractyes

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