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Record W3031167430 · doi:10.2147/imcrj.s255085

<p>Anterior Uveitis with Posterior Synechia and Iris Atrophy Following Implantation of a Phakic Intraocular Lens</p>

2020· article· en· W3031167430 on OpenAlexfundno aff
Mehrdad Mohammadpour, Mehdi Mazloumi, Masoud Khorrami‐Nejad

Bibliographic record

VenueInternational Medical Case Reports Journal · 2020
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsnot available
FundersYork University
KeywordsSynechiaMedicineIRIS (biosensor)OphthalmologyUveitisPhakic intraocular lensPupilSurgeryEye diseaseRefractive error

Abstract

fetched live from OpenAlex

PURPOSE: To describe a case with acute postoperative uveitis, posterior synechia and iris atrophy following iris-claw phakic intraocular lens (pIOL) implantation. METHODS: A case report. RESULTS: A 26-year-old man with high myopia had implantation of a -14.0 diopter, foldable, iris-claw Artiflex (model 401) anterior chamber pIOL (Ophtec B.V.) in both eyes. On the third postoperative day, the patient had significant postoperative inflammation in the left eye and received topical steroids and mydriatic eye drops. On the fifth postoperative day, the right eye had a round pupil and centered pIOL, but the left eye had an atrophic iris and dilated pupil with significant posterior synechias over the inferior half of the pupil. Despite intensive topical steroid application, the synechias remained one year after surgery. CONCLUSION: Severe uveitis with posterior synechia can occur after iris-claw pIOL implantation. We hypothesized that excessive iris tissue enclavation in the pIOLs haptics and large iridotomies may be an associated factor.

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)0.003
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0030.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0040.003
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 head0.012
GPT teacher head0.264
Teacher spread0.252 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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