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Record W4292363593 · doi:10.1097/icl.0000000000000928

Prosthetic Replacement of the Ocular Surface Ecosystem for Limbal Stem Cell Deficiency: A Case Series

2022· article· en· W4292363593 on OpenAlexaff
Anubhav Garg, Tanya Trinh, Bryan M. Wong, Michael Mimouni, Stephanie Ramdass, Jennifer Liao, Manokaraananthan Chandrakumar, Allan R. Slomovic, Clara C. Chan

Bibliographic record

VenueEye & Contact Lens Science & Clinical Practice · 2022
Typearticle
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsKensington HealthUniversity of Toronto
Fundersnot available
KeywordsMedicineStage (stratigraphy)Visual acuityOphthalmologySurgery

Abstract

fetched live from OpenAlex

OBJECTIVES: To assess outcomes of limbal stem cell deficiency (LSCD) in patients treated with Prosthetic Replacement of the Ocular Surface Ecosystem (PROSE). METHODS: Retrospective case series. Patients with LSCD who received PROSE treatment were included. Data including best-corrected visual acuity (BCVA) and LSCD staging before and after PROSE dispensing were collected to characterize each case. RESULTS: Five eyes of four patients were included. All patients were female, with an age range of 21 to 80 years. Each patient received a PROSE device with diameters ranging from 16 to 18.5 mm. Follow-up ranged from 11 to 29 months. Tolerated wear times ranged from 3.5 to 10 hr daily. Four eyes showed improved BCVA and unchanged LSCD staging as per the global consensus after PROSE treatment. Three of these eyes had stage 3 and one had stage 1C LSCD at diagnosis. The fifth eye had worse BCVA and recurrence of stage 3 LSCD post-living-related conjunctival limbal allograft transplant despite PROSE treatment. CONCLUSIONS: Prosthetic Replacement of the Ocular Surface Ecosystem may be a viable treatment for LSCD, including severe cases, because it can provide symptom relief and improve vision. Its customizability, as demonstrated in this study, is beneficial for troubleshooting issues with fitting. Future studies are needed to further assess PROSE as treatment for LSCD.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.785
Threshold uncertainty score0.829

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.056
GPT teacher head0.365
Teacher spread0.309 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations5
Published2022
Admission routes1
Has abstractyes

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