Cyclodialysis cleft repair: A multi‐centred, retrospective case series
Bibliographic record
Abstract
IMPORTANCE: There is a paucity of evidence analysing the treatment of cyclodialysis clefts. BACKGROUND: We describe outcomes following the treatment of this rare condition at six centres internationally. DESIGN: Retrospective case series. PARTICIPANTS: Thirty-six patients with a cyclodialysis cleft from 2003 to 2017 were recruited. METHODS: Clefts were treated with cycloplegic agents, laser therapy and/or surgery. MAIN OUTCOME MEASURES: Postoperative best recorded visual acuity (BRVA), intraocular pressure (IOP) and the rate of cleft closure. RESULTS: The mean age was 45 ± 17 years and 29 (80.6%) patients were male. One eye (2.8%) received only medical therapy, 5 (13.9%) received laser, 14 (38.9%) underwent surgery after laser failure and 16 (44.4%) eyes received exclusively surgery. Over 80% of eyes had a BRVA improvement of more than two lines. Closure was attained in 30 eyes (93.8%; n = 32), with postoperative stabilized IOP ≥ 12 mmHg in 29 eyes (80.6%; n = 36) and postoperative BRVA ≤20/50 in 20 eyes (58.8%; n = 34). Improved postoperative BRVA was related to better preoperative BRVA (P = 0.006) and preoperative IOP ≥ 4 mmHg (P = 0.03). There was no significant difference between treatment approach for IOP ≥ 12 mmHg (P = 0.85) or postoperative BRVA ≤20/50 (P = 0.80). Only two eyes at last follow-up required IOP lowering medication. CONCLUSIONS AND RELEVANCE: There was a high closure rate with most eyes eventually requiring surgery. Clinically significant improvements in BRVA were found in most eyes. Improved postoperative BRVA was significantly related to better preoperative BRVA and IOP.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".