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Record W2073853043 · doi:10.5301/ejo.5000445

A Comprehensive Clinical Study about Patching after Strabismus Surgery

2014· article· en· W2073853043 on OpenAlexaboutno aff
Wenqiu Zhang, Ji-Hong Zeng, Jinying Liao, Tao Yang, Jie Ren, Xiaozhou Zhou, Longqian Liu

Bibliographic record

VenueEuropean Journal of Ophthalmology · 2014
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsCryingMedicineStrabismusPerioperativeStrabismus surgeryPostoperative painProspective cohort studyPain scoreSurgeryAnesthesia

Abstract

fetched live from OpenAlex

PURPOSE: Patching of the operative eye is occasionally used in pediatric strabismus surgery. The primary purpose of this study was to investigate the psychological and physiologic effects of patching after operation by multifactorial methods. METHODS: We analyzed the perioperative behaviors of 61 children with strabismus conducted from June 2012 to July 2013 in this prospective longitudinal study. The children were randomized into 2 groups. Patients in the patching group underwent postoperative patching and others received no patching. Main outcome measures included The Faces Pain Scale-Revised score or numerical rating scales score, Children's Hospital of Eastern Ontario Pain Scale (CHEOPS) score, crying time, and preoperative and postoperative physiologic parameters. RESULTS: Crying time was significantly longer in the patching group than in the no patching group, but self-report scores showed no difference in the groups. Repeated-measures analysis of variance on ranks revealed that postoperative CHEOPS score was lower in the no patching group than in the patching group, whereas no physiologic parameters were significantly different in the 2 groups. CONCLUSIONS: Patching is not necessary for reducing postoperative pain or the risk of infection in children undergoing strabismus surgery.

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)0.006
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 score0.044
Threshold uncertainty score0.636

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
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 integrity0.0000.001
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 head0.065
GPT teacher head0.358
Teacher spread0.293 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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