A modified trabeculectomy technique with direct filtration into the Tenon's capsule
Bibliographic record
Abstract
OBJECTIVE: To report the surgical outcomes of a modified trabeculectomy technique involving implanting the Tenon's layer under the scleral flap. DESIGN: Prospective, interventional case series. PARTICIPANTS: 51 eyes with medically uncontrolled glaucoma were enrolled for this study. A new trabeculectomy technique, the Tenon's filtration technique for trabeculectomy (TFT-LEC) was used in 26 eyes, while a conventional procedure, normal trabeculectomy (N-LEC), was used for 25 eyes. METHODS: Intraocular pressure (IOP) control, the number of glaucoma medications, the need for additional interventions, and postoperative complications were assessed. RESULTS: Twelve months postoperatively, the mean IOP was 13.5 ± 0.5 mmHg in the TFT-LEC group and 15.4 ± 0.5 mmHg in the N-LEC group (p = 0.13). The TFT-LEC group required an average of 1.3 ± 1.0 additional glaucoma medications (21 cases required only ripasudil) postoperatively, with no cases of bleb needling revision or reoperation. The N-LEC group required an average of 1.7 ± 1.5 glaucoma medications (p = 0.43) compared to TFT-LEC group, eight cases (32%) required bleb needling revision (p = 0.002), and one case (4%) of reoperation (p = 0.49). The complications in the TFT-LEC group included shallow anterior chamber in six (23 %) cases (p = 1.00) compared to N-LEC group, choroidal detachment in two (8%) cases (p = 0.42), and anterior chamber hemorrhage in seven (27%) cases (p = 0.29). None of these complications affected visual function. CONCLUSIONS: This new technique involving implanting the Tenon's layer under the scleral flap may improve the postoperative outcomes of trabeculectomy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".