Calorimetric method for measuring phacoemulsification energy delivered during cataract surgery
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".