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Calorimetric method for measuring phacoemulsification energy delivered during cataract surgery

2025· article· en· W4411866190 on OpenAlexaff
Rosa Braga-Mele, Satish Yalamanchili, Sarah Makari, Guangyao Jia

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPhacoemulsificationUltrasoundUltrasound energyRepeatabilityBiomedical engineeringNuclear medicineMaterials scienceMedicineMathematicsSurgeryStatisticsRadiology

Abstract

fetched live from OpenAlex

PURPOSE: The purpose of this study was to determine the ultrasound (US) energy delivered to the eye during cataract surgery in Joules, a universal System Internacional (SI) unit of energy. This SI energy unit of measurement allows for comparison among various US modalities, phacoemulsification systems and other applications. SETTING: Laboratory. DESIGN: Experimental laboratory phacoemulsification energy dissipation model. METHODS: A calorimetric method of energy measurement was used, with a US handpiece, tip, and sleeve were held in place by an adaptor into a test chamber holding 0.5 g of water. Thermocouples measured the water temperature rise. The ultrasound power was tested in 10% increments from 10% to 100% with each ultrasound modality. RESULTS: High repeatability was observed in both the torsional and longitudinal modality, as indicated by a coefficient of variation less than 0.5 for each power level and the low standard deviation magnitude across the tests (0.020 J/s to 0.149 J/s in the torsional modality and 0.036 J/s to 0.150 J/s in the longitudinal modality, depending on ultrasound power levels). Third order polynomials demonstrated strong and non-linear correlations between ultrasound energy and power levels as indicated by high R2 values (>0.99). CONCLUSION: A calorimetric ultrasound energy measurement method provides greater insight into the total energy in the eye. Measuring total phacoemulsification energy in Joules allows for a standardized way to compare total energy across variable platforms and to be used as a potentially more clinically relevant proxy for assessing and improving clinical outcomes.

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)0.004
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.498
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.032
GPT teacher head0.317
Teacher spread0.285 · 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.

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

Citations2
Published2025
Admission routes1
Has abstractyes

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