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Record W1559351102 · doi:10.1155/2015/341450

Short-Term Intraocular Pressure Elevations after Combined Phacoemulsification and Implantation of Two Trabecular Micro-Bypass Stents: Prednisolone versus Loteprednol

2015· article· en· W1559351102 on OpenAlexaff
Qianqian Wang, Paul Harasymowycz

Bibliographic record

VenueJournal of Ophthalmology · 2015
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsMedicinePhacoemulsificationIntraocular pressureOphthalmologyTerm (time)CardiologyVisual acuity

Abstract

fetched live from OpenAlex

Objective. To compare the effects of prednisolone and of loteprednol after combined phacoemulsification and trabecular micro-bypass stent implantation (phaco-iStent). Methods. Patients who underwent phaco-iStent between April 2013 and November 2014 were identified by retrospective chart review. Postoperatively, they received either prednisolone (n = 38) or loteprednol (n = 58). Baseline data was compared. Primary outcomes including intraocular pressure (IOP) and number of glaucoma medications (NGM) were analyzed at preoperative visit, postoperative day 1, weeks 1-2, weeks 3-4, and months 2-3. Results. Both groups had similar preoperative parameters (p > 0.05). The mean IOP spike occurred at postoperative weeks 1-2 with an increase of 2.21 ± 7.30 mmHg in the loteprednol group and 2.54 ± 9.28 mmHg in the prednisolone group. It decreased by weeks 3-4 in both groups and continued to improve at months 2-3. NGM showed significant reduction (p < 0.0001) after the surgery and remained stable in both groups. No significant group effect or time-group interaction in IOP and NGM evolution was detected (p > 0.05). The proportions of patients needing paracentesis were similar between the two groups. Conclusion. Similar early IOP elevations after combined phaco-iStent occurred with both prednisolone and loteprednol. Facilitated glucocorticoid infusion, altered aqueous humor outflow, and local inflammation may be contributing factors.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.284
Threshold uncertainty score0.460

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.033
GPT teacher head0.324
Teacher spread0.291 · 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

Citations17
Published2015
Admission routes1
Has abstractyes

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