MétaCan
Menu
Back to cohort
Record W6995920746

Process Improvement: Perioperative Eye Protection under General Anesthesia for Nonocular Surgery

2023· article· en· W6995920746 on OpenAlexaboutno aff

Bibliographic record

VenueUA Campus Repository (The University of Arizona) · 2023
Typearticle
Languageen
FieldMedicine
TopicIntraoperative Neuromonitoring and Anesthetic Effects
Canadian institutionsnot available
Fundersnot available
KeywordsPerioperativeEye protectionEye surgeryClinical PracticeProcess (computing)
DOInot available

Abstract

fetched live from OpenAlex

Purpose. This process improvement project aimed to promote systematic perioperative eye protection for patients who undergo general anesthesia for nonocular surgery.Background. Perioperative eye injuries can cause distress from severe pain, vision loss, and even blindness. Anesthesia practice pattern is associated with perioperative eye protection outcomes. Eye protection beyond taping the eyes with general anesthesia is institution and practitioner dependent. Clinical evidence indicated that consistent anesthesia department-wide eye protection practice changes improve outcomes. Presenting current practice-based evidence could promote local process improvement toward a sustained result. Methods. The Ottawa Model of Research Use Theory guided this education and survey-based project design. All eligible anesthesia providers (n=12) were invited to review a PowerPoint presentation of an innovative four-step eye protection Bundle and its associated theory and clinical evidence. Their current practice and future practice change were surveyed for each component of the Bundle. Two data points—the participation rate and the Bundle acceptance rate in the post-survey—have to reach a predetermined threshold to consider implementing any component of the Bundle. In addition, the post-survey must receive a higher approval rate than the corresponding pre-survey question. Results. The first data point is the participation rate which is 67% (8/12), over the threshold of 50%. The second data point is that Five Bundle components reached the threshold of 60% in the post-presentation survey with three of the five components showing significant differences from pre-presentation survey. The three components are Individualized eye protection- “Need-based product choice,” “implementing an anesthesia department-wide perioperative eye complication treatment protocol, and collecting data on eye-protection outcomes. Conclusions. This project identified three priority areas of practice change for local process improvement.

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)0.031
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.028
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0020.002
Open science0.0020.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.239
Teacher spread0.225 · 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 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueUA Campus Repository (The University of Arizona)Same topicIntraoperative Neuromonitoring and Anesthetic EffectsFrench-language works237,207