New treatment algorithm for keratoconus and cataract: small-aperture IOL insertion with sequential topography-guided photorefractive keratectomy and simultaneous accelerated corneal crosslinking
Bibliographic record
Abstract
PURPOSE: To describe a new treatment algorithm aimed at optimizing refractive outcomes for patients with keratoconus and cataract. SETTING: Private practice in Sydney, Australia. DESIGN: Retrospective case series. METHODS: This procedural approach involves cataract extraction with small-aperture intraocular lens (IOL) insertion, IC-8 IOL (AcuFocus, Inc.), followed by topography-guided photorefractive keratectomy (T-PRK) with simultaneous corneal crosslinking (CXL). Cataract surgery was performed with an initial 2.4 mm clear corneal incision enlarged to 3.5 mm to accommodate IC-8 IOL insertion. Once eyes demonstrated stable corneal tomography and refraction, T-PRK was performed using Schwind excimer laser (500 Hz) with the Vancouver custom topographical neutralization technique, aiming to achieve low myopia. CXL was performed immediately after T-PRK using Optolink hypotonic riboflavin with LIGHTLink-CXL (Lightmed) with 5.4 J total energy delivered at an 18 mw/cm2 irradiance. RESULTS: Outcomes of 4 eyes are reported with all achieving rigid gas-permeable (RGP) contact lens independence, improved corrected distance visual acuity (CDVA) and uncorrected distance visual acuity (UDVA), and regularization of corneal curvature with cone reduction. The mean CDVA improved from 0.43 preoperatively to 0.07 postoperatively (P = .00), and the mean UDVA improved from 0.81 preoperatively to 0.29 postoperatively (P = .04). Postoperative UNVA ranged from N.8 to N.12. CONCLUSIONS: This treatment algorithm demonstrates unique combination of existing corneal and cataract surgical procedures to achieve satisfactory refractive outcomes and RGP contact lens independence in patients with keratoconus and cataract.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".