Argentinian flag sign during refractive laser-assisted cataract surgery – A case report
Bibliographic record
Abstract
Purpose: The Argentinian flag sign, or radial capsular tear extensions, is a rare complication when performing capsulorhexis during cataract surgery. Identifying and managing this complication early is important to prevent the tear from propagating around the periphery leading to posterior capsular rupture or vitreous loss. Observations: The Argentinian flag sign was previously reported in a case of femtosecond laser-assisted cataract surgery (FLACS). However, our report presents the first case after FLACS using the Catalys™ Precision Laser System, a platform which has been associated with a larger percentage of complete capsulotomies when compared to other platforms. Radial extensions of the capsular tear were observed in a 27-year-old male patient with an intumescent cataract in left eye. The complication was managed by manually redirecting and completing the radial extension flaps, along with delicate phacoemulsification and manual cutting of capsular edge in areas with significant capsular-IOL overlap. Conclusions and importance: Our case report highlights that despite the Catalys™ Precision Laser System success rates, radial tears may occur, especially in highly pressurized intumescent cataract. Therefore, surgeons must be prepared to optimize the surgical techniques to prevent the occurrence of this complication, as well as identify and manage it when it presents.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".