Cyclodialysis cleft repair: A multi‐centred, retrospective case series
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it