Planning Strabismus Surgery: How to Avoid Pitfalls and Complications
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Good surgical results following strabismus surgery depend on several factors. In this article, detailed steps for planning strabismus surgery will be reviewed for basic horizontal strabismus surgery, vertical, and oblique muscle surgeries. The thought process behind each case will be presented to help in selecting the best surgical approach to optimize postoperative results. PATIENTS AND METHOD: The surgical planning for strabismus will be developed with clinical examples from easy cases to more complex ones. Preoperative pictures of the ocular alignment are an integral part of planning surgery and help in documenting the strabismus before and after surgery. RESULTS: Three cases of strabismus cases will be reviewed with several key factors for planning surgery, including visual acuity, refractive error, potential for stereovision, and risk of postoperative diplopia. The most important factor is accurate orthoptic measurements. The surgical planning for each patient is detailed along with preoperative pictures. CONCLUSION: Strabismus surgery results can be improved by careful preoperative planning. The surgeon has the ability to discern potential pitfalls that can alter the surgical outcome. Surgical planning allows a dedicated time of reflection before surgery, foreseeing potential problems, and avoiding them during the 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".