Nuances of preoperative care before cataract extraction. What do we overlook when performing biometry, calculating IOL power, and examining the eye?
Bibliographic record
Abstract
Modern cataract surgery is increasingly regarded as a refractive procedure. The focus has shifted from practicing and refining surgical steps towards personalized intraocular lens (IOL) choice based on the eye parameters of each individual. The best possible refractive outcome is now the priority. All components of biometry contribute to IOL power calculation accuracy. Therefore, any errors, a method of evaluating each parameter, and a technician’s experience are important. In addition, refraction, macular disorders, and prior surgical procedures affect IOL choice, preoperative care, and the extent of surgery. Moreover, subsequent changes in the IOL position that results in refractive errors after cataract surgery (this depends on the formula for IOL power calculation) should also be considered. The accuracy of IOL power calculation is affected by the inaccuracy of current biometry techniques and postoperative changes of the globe. Personalization of theoretical formulas provides better accuracy of IOL power calculations to meet modern trends in intraocular correction. Supplements containing macular pigments prevent macular degeneration and protect the macular zone. Keywords: cataract, refraction, cataract surgery, biometry, IOL power calculation, IOL power calculation formulas, retina, age-related macular degeneration. For citation: Movsisyan A.B., Egorov A.E. Nuances of preoperative care before cataract extraction. What do we overlook when performing biometry, calculating IOL power, and examining the eye? Russian Journal of Clinical Ophthalmology. 2021;21(3):159–163 (in Russ.). DOI: 10.32364/2311-7729-2021-21-3-159-163.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| 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".