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Record W2036829417 · doi:10.4021/jocmr2009.12.1280

The Effects of Injection Site on the Reflux Following Intravitreal Injections

2009· article· en· W2036829417 on OpenAlexvenueno aff
Burak Turgut

Bibliographic record

VenueJournal of Clinical Medicine Research · 2009
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineTriamcinolone acetonideQuadrant (abdomen)PegaptanibOphthalmologyBevacizumabRefluxAcetonideSurgeryRanibizumabInternal medicineChemotherapy

Abstract

fetched live from OpenAlex

BACKGROUND: This study aimed to investigate the effects of injection site on the reflux after intravitreal injection. METHODS: One hundred and eighty eyes undergoing intravitreal injection including 0.1 ml of triamcinolone acetonide or bevacizumab or pegaptanib were divided to six groups (30 patients in each group) to compare the vitreal reflux after injection using superotemporal versus inferotemporal quadrant. The amount of intraoperative reflux was estimated by measuring the width of the subconjunctival bleb. An interventional, prospective, comparative clinical trial was applied. RESULTS: The mean bleb width as the reflux amount after injection of three drugs was statistically less after the inferotemporal injection (1.50 ± 0.94 mm for triamcinolone acetonide, p < 0.001; 1.60 ± 1.07 mm for bevacizumab, p < 0.001; and 1.77 ± 0.94 mm for pegaptanib, p = 0.001) than those in eyes undergoing the superotemporal injection (3.20 ± 1.63 mm for triamcinolone acetonide; 3.07 ± 1.53 mm for bevacizumab; and 2.80 ± 1.32 mm for pegaptanib). CONCLUSIONS: The injection through inferotemporal quadrant provides statistically significant less vitreal reflux for intravitreal drug injection. KEYWORDS: Intravitreal injection; Injection site; Reflux.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0020.000

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 head0.133
GPT teacher head0.554
Teacher spread0.421 · 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 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

Citations12
Published2009
Admission routes1
Has abstractyes

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