SUTURELESS CLOSURE OF 23- AND 25-GAUGE LEAKING SCLEROTOMIES WITH THE SCLERAL NEEDLING TECHNIQUE
Bibliographic record
Abstract
PURPOSE: To describe and evaluate the efficacy of a novel technique, scleral needling (SN), for securing 23- and 25-gauge leaking sclerotomies in microincision vitrectomy surgery. METHODS: A retrospective comparative review of consecutive cases of 23- and 25-gauge pars plana vitrectomy performed by a single vitreoretinal surgeon before the introduction of the SN technique (pre-SN; November 2016 to January 2017) and after the introduction of the SN technique (post-SN; November 2017 to January 2018) was conducted. The SN technique was implemented as an alternative to suturing, using a 30-gauge needle inserted perpendicularly through the full thickness of the sclera adjacent to the scleral opening, with the needle then immediately removed and sclerotomy closure confirmed. RESULTS: A total of 203 eyes, 105 from pre-SN and 98 from post-SN, were included in the study. The number of eyes requiring suture closure was significantly reduced from 39% in the pre-SN group to 2% in the post-SN group (P < 0.001). The mean postoperative intraocular pressure and incidence of hypotony on Days 1 to 2, Days 3 to 20, and Days 21 to 50 was not significantly different between the pre-SN and post-SN groups. No major complications associated with the SN technique were noted during the study period. CONCLUSION: The SN technique is a safe and simple method for effectively securing leaking sclerotomies in microincision vitrectomy surgery.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".