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Protective effect of soft contact lenses after Boston keratoprosthesis

2015· article· en· W1784241779 on OpenAlexaff
Leah L Kammerdiener, Jaime L. Speiser, James V. Aquavella, Mona Harissi‐Dagher, Claes H. Dohlman, James Chodosh, Joseph B. Ciolino

Bibliographic record

VenueBritish Journal of Ophthalmology · 2015
Typearticle
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMedicineKeratoprosthesisContact lensOphthalmologySurgeryVisual acuity

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate associations between preoperative diagnosis, soft contact lens (SCL) retention and complications. METHODS: A retrospective chart review was conducted of 92 adult patients (103 eyes) who received a Boston keratoprosthesis type I at the Massachusetts's Eye and Ear Infirmary or the Flaum Eye Institute. Records were reviewed for preoperative diagnosis, SCL retention and subsequent complications. Preoperative categories included 16 autoimmune (Stevens-Johnson syndrome, ocular cicatricial pemphigoid, rheumatoid arthritis and uveitis), 9 chemical injury and 67 'other' (aniridia, postoperative infection, dystrophies, keratopathies) patients. RESULTS: 50% of the lenses had been lost the first time after about a year. A subset (n=17) experienced more than 2 SCL losses per year; this group is comprised of 1 patient with autoimmune diseases, 2 patients with chemical injuries and 14 patients with 'other' diseases. The preoperative diagnosis was not predictive of contact lens retention. However, multivariate analysis demonstrated that the absence of a contact lens was an independent risk factor for postoperative complications, such as corneal melts with or without aqueous humour leak/extrusion and infections. CONCLUSIONS: Presence of a contact lens after Boston keratoprosthesis implantation decreases the risk of postoperative complications; this has been clinically experienced by ophthalmologists, but never before has the benefit of contact lens use in this patient population been statistically documented.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.317
Threshold uncertainty score0.440

Codex and Gemma teacher scores by category

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

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 head0.022
GPT teacher head0.286
Teacher spread0.265 · 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

Citations45
Published2015
Admission routes1
Has abstractyes

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