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Record W2045433542 · doi:10.1097/ico.0b013e31817c41e3

Combined Use of Superficial Keratectomy and Subconjunctival Bevacizumab Injection for Corneal Neovascularization

2008· article· en· W2045433542 on OpenAlexaff
Cynthia X. Qian, Irit Bahar, Eliya Levinger, David Rootman

Bibliographic record

VenueCornea · 2008
Typearticle
Languageen
FieldMedicine
TopicCorneal Surgery and Treatments
Canadian institutionsUniversité de MontréalHôpital Notre-Dame
Fundersnot available
KeywordsMedicineBevacizumabCorneal neovascularizationOphthalmologyNeovascularizationVisual acuityCorneaSurgeryChemotherapyInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To report the effect of superficial keratectomy combined with subconjunctival bevacizumab injection in 2 cases of corneal neovascularization (NV). METHODS: An interventional case series was undertaken on 2 patients with corneal NV: 1 due to sclerokeratitis secondary to rheumatoid arthritis and the other due to Terrien marginal degeneration. Both patients underwent superficial keratectomy combined with subconjunctival bevacizumab injection (2.5 mg/0.1 mL). RESULTS: Corneal NV regressed with the surgical removal and showed no signs of recurrence after 3 months of follow-up. Both patients reported dramatic subjective improvement in their vision within 1-2 weeks. Best corrected visual acuity improved in 1 patient. CONCLUSION: The combination of superficial keratectomy with subconjunctival injection of bevacizumab may offer a new strategy for the treatment of superficial corneal NV.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.053
GPT teacher head0.252
Teacher spread0.199 · 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 designNon-randomized trial
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

Citations30
Published2008
Admission routes1
Has abstractyes

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