Anatomical and functional outcomes of retinal detachment associated with nontraumatic giant retinal tears compared to simple rhegmatogenous retinal detachment
Bibliographic record
Abstract
BACKGROUND: To compare the functional and anatomical outcomes of primary surgery in patients with giant retinal tear (GRT)-associated retinal detachment (GRT-RD) to patients with simple rhegmatogenous RD (RRD). METHODS: This is a retrospective study at the CHU de Québec - Université Laval. Medical records of all consecutive patients operated for RD between 2014 and 2018 were reviewed. Patients with GRT-RD and RRD were included. Preoperative, intraoperative, and postoperative data were compared between both groups, including extension of giant tears, number of RD quadrants, preoperative macula and lens status, type of surgery, best corrected visual acuity (BCVA) in logarithm of the minimum angle of resolution (logMAR) preoperatively and at follow-up, and single surgery anatomical success (SASS). RESULTS: There were 39 patients (1.7%) with GRT-RD and 1661 patients (74%) with RRD. Median [Q1, Q3] ages were 59 [52, 62] years and 62 [56, 69] years (p = 0.003), while number of affected quadrants were 2 [2, 3] and 2 [2, 3] (p = 0.96) in GRT-RD and RRD patients, respectively. In GRT-RD patients, GRT size was 120 [90, 150] degrees. Final BCVA was 0.30 [0.10, 0.30] and 0.30 [0.10, 0.40] (p = 0.76) in GRT and RRD patients, respectively. SSAS was 82% (32/39) in the GRT-associated-RD group and 90% (1495/1661) in the RRD group (p = 0.10). After correcting for other preoperative factors, GRT was a risk factor for worse SSAS (odds ratio: 0.422, p = 0.047). CONCLUSIONS: GRT-RD is still challenging to treat, and our results suggest that it is a risk factor for poorer SSAS.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".