Surgical management of negative dysphotopsia
Bibliographic record
Abstract
PURPOSE: To evaluate curative and preventative surgical strategies for negative dysphotopsia. SETTING: Private practice, Los Angeles, California, USA. DESIGN: Retrospective case series. METHODS: Patients with self-reported chronic negative dysphotopsia had corrective surgery as the therapeutic group. Second eye surgery, in cases with negative dysphotopsia in the previously operated eye, comprised the preventative group. Chronologically, several surgical strategies were used, including bag-to-bag intraocular lens (IOL) exchange, reducing posterior chamber depth, piggyback secondary IOL placement, bag-to-sulcus IOL exchange, and reverse optic capture. The primary outcome measure was improvement of negative dysphotopsia by 3 months postoperatively. RESULTS: The therapeutic group comprised 40 eyes of 37 patients; 76.6% of causative IOLs were acrylic and 23.4% were silicone and all were bag-fixated. There were 21 eyes in the preventative group of which 11 were second eyes from the therapeutic group; the remaining 10 did not require surgery for the symptomatic eye. Successful outcomes for each surgical strategy were as follows: bag-to-bag IOL exchange (0/5), a reduction in posterior chamber depth with iris suture fixation of the bag-haptic complex (0/1), piggyback secondary IOL (8/11), secondary reverse optic capture (21/22), ciliary sulcus posterior chamber IOL exchange (7/8), and primary reverse optic capture (21/21). CONCLUSIONS: Negative dysphotopsia was associated with acrylic or silicone IOLs of either square- or round-edge design. Negative dysphotopsia was reduced, eliminated, or prevented when the IOL optic overlaid the anterior capsulotomy rather than when the capsule edge overlaid the optic. Bag-to-sulcus IOL exchange and reverse optic capture were highly successful in managing or preventing negative dysphotopsia.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".