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Record W3021069496 · doi:10.1097/iop.0000000000001690

The Role of Postoperative Bandage Contact Lens in Patients Undergoing Fasanella-Servat Ptosis Repair

2020· article· en· W3021069496 on OpenAlexaff
Robert S. Adam, Jonah N. Gould, Sivisan Suntheralingam, Forough Farrokhyar

Bibliographic record

VenueOphthalmic Plastic and Reconstructive Surgery · 2020
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsMcMaster UniversityUniversity of Toronto
Fundersnot available
KeywordsMedicineBlurred visionPtosisBandageSurgeryEye diseaseProspective cohort studyRandomized controlled trialOphthalmology

Abstract

fetched live from OpenAlex

PURPOSE: To determine whether a bandage contact lens (BCL) improves patient comfort in the postoperative period in patients undergoing ptosis repair using the Fasanella-Servat technique, compared with no BCL. METHODS: In this prospective, randomized, double-masked, comparison study, all patients had bilateral Fasanella-Servat surgery. A total of 30 patients were randomized to receive a BCL in one eye and no BCL in the other eye. Patient discomfort was measured as the primary outcome using the Eye Sensation Scale. Blurred vision was measured as a secondary outcome using selected questions from the Ocular Surface Disease Index. The surgeries were performed by 2 surgeons (J.T.H and R.S.A). Outcomes were measured one week following the procedure. RESULTS: Patients reported significantly less discomfort in the eye receiving a BCL, with only 13.3% ranking discomfort as "moderate" or "severe," compared with the eye not receiving BCL, where 63.3% of patients rated discomfort as "moderate" or "severe" (p < 0.001). There was no significant difference in patient-reported blurred vision between the 2 groups (p = 0.520). CONCLUSIONS: The use of a bandage contact lens after Fasanella-Servat procedure for ptosis repair is recommended as it improves patient comfort. In addition, it has no detrimental effect on patient-reported blurring of vision.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.210
Teacher spread0.197 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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