DYE EXTRUSION TECHNIQUE (DE-TECH)
Bibliographic record
Abstract
In Brief Objective: To investigate the effectiveness of a new technique for the identification of occult retinal breaks in vitrectomy retinal detachment repair. Dye Extrusion Technique involves injection of sub retinal dye and extrusion through the unidentified breaks using perfluorocarbon liquid. Method: Retrospective case note analysis. Main outcome measures: rate of break detection, rate of use of local retinopexy, and short-term gas tamponade alone; Secondary outcomes: anatomical success at 3 months with no long-term tamponade, final visual acuity, progression of proliferative vitreoretinopathy. Results: Thirty-eight percent of eyes had proliferative vitreoretinopathy of grade C1 or worse. Twenty-five percent underwent primary retinal detachment repair surgery. The remainder underwent repeat operations for previously failed surgery. Breaks were found in 89% of eyes and 81% underwent only local retinopexy and gas tamponade. The overall anatomical success rate was 85% (40/47) which included four cases in which the retina was fully attached awaiting removal of silicone oil. No cases had unexplained loss of vision. No progression of proliferative vitreoretinopathy was noted. Conclusions: Results suggest that Dye Extrusion Technique may be an effective technique for the identification of occult retinal breaks and may facilitate simple techniques of detachment repair. No evidence of retino-toxicity from the use of trypan blue was found but the study had limited power to detect adverse effects. Unidentified retinal breaks are the most prevalent cause of failure in retinal detachment surgery. Dye Extrusion Technique involves injection of subretinal dye and extrusion through unidentified breaks. Results suggest that Dye Extrusion Technique may be an effective technique for identification of occult retinal breaks and may facilitate simple techniques of detachment repair.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".