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Record W2437985874

[Corneal changes after phacoemulsification with a corneal versus a scleral tunnel incision].

2006· article· en· W2437985874 on OpenAlexaffabout
Adi Michaeli, David S. Rootman, Allan R. Slomovic

Bibliographic record

VenuePubMed · 2006
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicinePhacoemulsificationCorneaOphthalmologyCorneal pachymetryCorneal endotheliumSignificant differenceCataract surgerySurgeryCorneal topographyVisual acuity
DOInot available

Abstract

fetched live from OpenAlex

AIM: To compare central corneal thickness and endothelial cell loss after phacoemulsification with clear cornea and scleral tunnel incisions. SETTING: Ophthalmology Department, Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada. METHOD: Fifty-one eyes of 51 patients were randomly assigned to one of the two study groups. All patients had central corneal thickness measured preoperatively and on post-op days 1, 7, 30 and 90. Specular microscopy was performed preoperatively and at 3 months after surgery. RESULTS: Cumulative mean central endothelial cell count before surgery was 2,355 +/- 360 mm2 and 2,305 +/- 376 mm2 post-op. Mean percentage cell loss was 0.82% +/- 19.7%. For the scleral tunnel group it was 1.8% +/- 21.5%, and for the clear cornea group 0.13% +/- 18.3% (p>0.05). Two-way ANOVA demonstrated no effect of type of incision and surgeon on the endothelial cell loss. Pearson correlation coefficients between phaco power and cell loss calculated for each of the incisions and for each of the surgeons was not significant. Corneal thickness increased significantly in all measurements post-op, and returned to baseline by 3 months. There was no difference in the pachymetry change between the two study groups. CONCLUSIONS: Clear cornea and scleral tunnel incisions seem to result in no significant difference in endothelial cell loss and or central corneal thickness at 3 months post-op.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.487

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 head0.027
GPT teacher head0.221
Teacher spread0.194 · 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

Citations1
Published2006
Admission routes2
Has abstractyes

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