MétaCan
Menu
Back to cohort
Record W4282976337 · doi:10.1097/icb.0000000000001293

MACULAR HOLE HYDRODISSECTION TECHNIQUE WITH HUMAN AMNIOTIC MEMBRANE FOR REPAIR OF LARGE MACULAR HOLES

2022· article· en· W4282976337 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRetinal Cases & Brief Reports · 2022
Typearticle
Languageen
FieldMedicine
TopicRetinal and Macular Surgery
Canadian institutionsToronto Western HospitalUniversity Health NetworkPublic Health OntarioUniversity of Toronto
Fundersnot available
KeywordsCannulaMacular holeMedicineRetinal pigment epitheliumOphthalmologyAmnionRetinalVitrectomySurgeryBiologyPregnancyFetus

Abstract

fetched live from OpenAlex

PURPOSE: To describe a combined surgical technique using the macular hole hydrodissection (MHH) with human amniotic membrane for repair of large macular holes. METHODS: A step-by-step procedure and a surgical video using the combined MHH and human amniotic membrane technique are presented. DESCRIPTION AND TECHNIQUE: As the first step, the MHH separates the adhesions of the macular hole to the underlying retinal pigment epithelium with a soft-tipped cannula through proportional reflux followed by gentle passive aspiration. The human amniotic membrane graft is marked to identify the nonsticky epithelial side and ensure that the stromal layer (sticky and nonshinny) is facing downward toward the retinal pigment epithelium. The graft is then tucked into the space created with MHH between the macular hole edges and the retinal pigment epithelium with closed forceps to decrease the likelihood of the graft from dislocating postoperatively. CONCLUSION: The MHH in combination with the human amniotic membrane is a practical and effective technique for addressing challenging large macular holes.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.244
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.014
GPT teacher head0.279
Teacher spread0.265 · 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