MétaCan
Menu
Back to cohort
Record W3035734731 · doi:10.1097/icb.0000000000001024

INTERNAL CHANDELIER-ASSISTED MACULAR BUCKLING FOR MYOPIC FOVEOSCHISIS

2020· article· en· W3035734731 on OpenAlexaff
Parampal S. Grewal, Mark E. Seamone, Mark Greve, Adam P Deveau, R. Rishi Gupta

Bibliographic record

VenueRetinal Cases & Brief Reports · 2020
Typearticle
Languageen
FieldMedicine
TopicRetinal and Macular Surgery
Canadian institutionsDalhousie UniversityUniversity of Alberta
Fundersnot available
KeywordsMedicineOptical coherence tomographyBuckleOphthalmologyVisual acuityMacular holeOptometryVitrectomy

Abstract

fetched live from OpenAlex

PURPOSE: To present a surgical technique and case presentation of internal chandelier-assisted macular buckling for myopic foveoschisis. METHODS: Review of patient clinical features, visual acuity, and optical coherence tomography results after internal chandelier-assisted macular buckling for myopic foveoschisis. RESULTS: A 48-year-old highly myopic woman (axial length 29.85 mm) underwent internal chandelier-assisted macular buckling for myopic foveoschisis with macular detachment. The best-corrected visual acuity improved from 20/150 to 20/40. Postoperative optical coherence tomography confirmed central buckle positioning and demonstrated resolved foveoschisis and macular detachment. There were no complications. CONCLUSION: Internal chandelier-assisted macular buckling is a valuable tool to optimize buckle position and patient outcomes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0000.000

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 head0.041
GPT teacher head0.294
Teacher spread0.253 · 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.

Study designNot applicable
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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueRetinal Cases & Brief ReportsSame topicRetinal and Macular SurgeryFrench-language works237,207