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Record W2887133245 · doi:10.1016/j.ajoc.2018.08.003

Patch graft using collagen matrix (Ologen) for glaucoma drainage device exposure in a patient with Boston Keratoprosthesis type 1

2018· article· en· W2887133245 on OpenAlexaff
Samir Jabbour, Mark R. Lesk, Mona Harissi‐Dagher

Bibliographic record

VenueAmerican Journal of Ophthalmology Case Reports · 2018
Typearticle
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsCentre Hospitalier de l’Université de MontréalHôpital Maisonneuve-RosemontUniversité de Montréal
Fundersnot available
KeywordsKeratoprosthesisMedicineGlaucomaSurgeryOphthalmologyMatrix (chemical analysis)Visual acuityMaterials science

Abstract

fetched live from OpenAlex

PURPOSE: To report the first successfully treated case of recurrent tube exposure in a patient with the Boston Keratoprosthesis type 1 with a collagen matrix patch graft (Ologen). OBSERVATIONS: A 50 year-old female with a Boston Keratoprosthesis type 1 and a history of Axenfeld-Reiger syndrome presents to our department with recurrent glaucoma drainage device exposure in her left eye. After failed spontaneous closure with topical antibiotics and lubricants, she undergoes tube exposure repair using an Ologen patch graft. Surgery was successful and the patient did not have any recurrence up to last follow-up two years post-operatively. CONCLUSION: Collagen matrix patch graft seems to be advantageous in treating glaucoma tube exposure in the Boston KPro eye, which is often a more challenging entity to treat. IMPORTANCE: Collagen matrix patch graft could be considered as a primary patch graft in treating tube exposure in eyes with the Boston KPro.

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)0.000
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.024
GPT teacher head0.322
Teacher spread0.299 · 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 designCase report
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

Citations4
Published2018
Admission routes1
Has abstractyes

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