Macular Hemorrhage Due to Age-Related Macular Degeneration or Retinal Arterial Macroaneurysm: Predictive Factors of Surgical Outcome
Bibliographic record
Abstract
Objective: The study aimed to determine the outcomes and prognostic factors of vitrectomy, subretinal injection of tissue-plasminogen activator and gas tamponade in macular hemorrhage (MaH) due to age-related macular degeneration (AMD) or retinal arterial macroaneurysm (RAM). Methods: The study design utilized a multicentric retrospective case series design of consecutive patients undergoing surgery between 2014 and 2019. Results: A total of 65 eyes from 65 patients were included in the study. Surgery was performed after a mean period of 7.1 days. Displacement of MaH was achieved in 82% of the eyes. Mean best-corrected visual acuity (BCVA) improved from 20/500 to 20/125 at month(M)1 and M6 (p < 0.05). At M6, BCVA worsening was associated with an older age at diagnosis (p = 0.0002) and higher subretinal OCT elevation of MaH (p = 0.03). The use of treat and extend (TE) (OR = 16.7, p = 0.001) and small MaH fundus size (OR = 0.64 and 0.74 for horizontal and vertical fundus size, p < 0.05) were predictive of a higher likelihood of obtaining a countable BCVA at M1. Baseline BCVA was predictive of postoperative BCVA (p < 0.05). Retinal detachment and MaH recurrence occurred in 3% and 9.3% of cases at M6. Conclusion: MaH surgery stabilizes or improves BCVA in 85% of cases. Younger age at diagnosis, better baseline BCVA figures, smaller subretinal MaH height and use of TE regime were predictive of the best postoperative outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".