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Record W4283701023 · doi:10.1186/s13104-022-06125-w

Effect of cryotherapy on pain scores and satisfaction levels of patients in cataract surgery under topical anesthesia: a prospective randomized double-blind trial

2022· article· en· W4283701023 on OpenAlexaff
Marzieh Beigom Khezri, Abbas Akrami, Matina Majdi, Bijan Gahandideh, Navid Mohammadi

Bibliographic record

VenueBMC Research Notes · 2022
Typearticle
Languageen
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsCoquitlam College
FundersQazvin University of Medical Sciences
KeywordsMedicineCryotherapyAnesthesiaRandomized controlled trialPatient satisfactionPostoperative painCataract surgeryHeart rateSedationClinical trialSurgeryBlood pressure

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the effects of cryotherapy on pain scores and satisfaction levels of patients during cataract surgery under topical anesthesia. Eighty patients aged between 55 and 75 years scheduled for cataract surgery were randomly allocated to two study groups to receive topical anesthesia with cryotherapy (TC) or topical anesthesia alone (T) groups. Visual analog pain scores, patient satisfaction level, hemodynamic parameters, and quality of operating conditions were recorded. RESULTS: Cryotherapy significantly reduced VAS pain scores during surgery (P = 0.014). Although no significant difference in postoperative pain scores, opioid consumption, heart rate, and mean arterial blood pressure was seen in the postoperative period. The surgeon reported better quality of operating conditions in the TC group (P = 0.018). Cryotherapy as a complementary method with topical anesthesia reduced pain scores of patients during surgery. It also produced a better quality of operating conditions for surgeons. There was no significant difference in either postoperative pain scores or opioid consumption. Trial registration This trial was registered at Iranian clinical trial registering: IRCT registration number: IRCT2017052734091N2.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.088
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
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 head0.109
GPT teacher head0.395
Teacher spread0.286 · 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

Citations9
Published2022
Admission routes1
Has abstractyes

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