Intraocular Foreign Body Removal by 23-Gauge Micro Incision Vitrectomy Surgery and Back Flush Flute Needle: A Case Series Study
Bibliographic record
Abstract
BACKGROUND: This study aimed to consider a new technique to extract an intraocular foreign body by 23-gauge micro incision vitrectomy surgery (23G-MIVS). METHOD: This case series was done on Patients with intraocular foreign bodies and cataract during 2012-2015 in Tabriz University of Medical Sciences. Phacoemulsification and aspiration of lens nucleus, intraocular lens implantation, 23G-MIVS, and extraction of the foreign body were performed on patients. The foreign body was removed through a posterior capsulor hexis, anterior continuous curvilinear capsulorhexis, and a corneal incision. In all cases, the foreign body was safely removed through the corneal incision with back flush Flute Needle, and IOL was implanted and well positioned. The surgical incision did not require suturing. RESULTS: This technique was successful for the patients and the corneal endothelial cell density was maintained over 2000 cells/mm2 in all cases during recent follow-up examinations. CONCLUSION: We found that 23G-MIVS with this technique is suitable to remove the foreign body. It is safe, without complications, and can be used without enlarging the 23-gauge sclerotomy.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".