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Record W3156870917 · doi:10.5507/bp.2021.022

No significant retinal damage induced by major orthopedic surgery - a pilot study

2021· article· en· W3156870917 on OpenAlexaff
Tomas Parizek, Roman Škulec, I Liehneová, Petr Prasek, Vladimír Černý

Bibliographic record

VenueBiomedical Papers · 2021
Typearticle
Languageen
FieldMedicine
TopicIntraoperative Neuromonitoring and Anesthetic Effects
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicinePerioperativeSubclinical infectionRetinalOrthopedic surgeryNerve fiber layerSurgeryObservational studyProspective cohort studyIncidence (geometry)AnesthesiaClinical trialOphthalmologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Perioperative visual loss is one of the rare but devastating complications of anesthesia and surgery. The incidence of less severe or even subclinical postoperative visual dysfunction is unknown. Therefore, we decided to perform a pilot prospective observational clinical study to evaluate whether structural changes of the retina can be detected in patients undergoing elective orthopaedic surgery by optical coherence tomography (OCT). METHODS: Adult patients indicated for elective knee replacement surgery with the absence of known retinal or optic nerve disease were included. Each patient underwent baseline OCT examination of the eyes one day before surgery and it was repeated 4-7 days after the surgery. The surgery was done under general and epidural anesthesia. RESULTS: A total of 18 patients (6 men and 12 women) at the age of 70.8±7.1 years were enrolled. We found statistically significant changes in the Macular central thickness and in a few areas of the Retinal Nerve Fiber Layer between the baseline and postoperative measurements. CONCLUSIONS: Even though we found significant changes in some parameters, we did not confirm that general anesthesia and/or surgical damage causes significant damage of the retina using OCT measurement. TRIAL REGISTRATION: ClinicalTrials.gov (NCT04311801).

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.869
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.028
GPT teacher head0.283
Teacher spread0.255 · 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 designBench or experimental
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

Citations1
Published2021
Admission routes1
Has abstractyes

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