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Record W2170342759 · doi:10.5539/gjhs.v7n6p88

Safety and Efficacy of Propranolol in Comparison With Combination of Fentanyl and Ketamine as Premedication in Cataract Surgery Under the Topical Anesthesia

2015· article· en· W2170342759 on OpenAlexvenueno aff
Farhad Fazel, Hamidhajigholam Saryazdi, Leila Rezaei, Mohammad Mahboubi

Bibliographic record

VenueGlobal Journal of Health Science · 2015
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsnot available
Fundersnot available
KeywordsPremedicationMedicineAnesthesiaFentanylKetaminePropranololHemodynamicsCataract surgerySurgery

Abstract

fetched live from OpenAlex

This study evaluated the safety and effects of propranolol as a premedication before cataract surgery and compared them with the usual combination doses of fentanyl and ketamine. Among all reffered patients to Feiz Hospital of Esfahan for cataract surgery, 122 patients between Mar to Sep 2010 were enrolled in this study and randomly allocated into one of the following equal groups: 40 mg propranolol, 2 hours before surgery and combination of 15 mg ketamine and 50 µg fentanyl l. 5 min before surgery. The ability to control of hemodynamic instabilities caused by stress and to gain patients satisfaction was compared between two groups. Also, the efficacy of each premedication to control of hemodynamic changes during surgery were evaluated and compared. No significant differences were seen in the patients satisfaction and controlling of stress induced hemodynamic changes between two groups (P>0.05). However, patients in ketamine + fentanyl group showed more nausea and less pain during and after surgery. Moreover, no significant adverse effects were reported during and after the surgery. Our results demonstrated that propranolol can be used safely as a premedication in cataract surgery in the comparable efficacy to ketamine plus fentanyl premedication.

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)0.003
metaresearch head score (Gemma)0.000
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 score0.015
Threshold uncertainty score0.191

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.036
GPT teacher head0.338
Teacher spread0.302 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

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