Localizing the Lost Rectus Muscle Using the Connective Tissue Framework: Revisiting the Tunnel Technique
Bibliographic record
Abstract
PURPOSE: To describe a technique for localizing a lost rectus muscle during strabismus or retinal surgery or following trauma. METHODS: In this single center, retrospective chart review, 5 patients were identified between January 2012 and June 2016 with a lost rectus muscle; 3 during strabismus surgery and 2 post trauma. The inclusion criteria included a lost rectus muscle during strabismus surgery, or a disinserted/lacerated rectus muscle following ocular/orbital trauma. The primary outcome measure was successful reattachment of the rectus muscle. RESULTS: The lost rectus muscle was identified in each patient and reattached to the globe by gently applying traction anteriorly at the conjunctiva/Tenon edge using double-pronged skin hooks and following the path of the rectus muscle through its Tenon capsule tunnel where it remained attached by suspensory ligaments. There was no instance where orbital fat was obscuring or blocking the view of the lost rectus muscles. There were no other complications associated with the procedure. CONCLUSIONS: The authors describe a simple and effective method in 5 patients to localize a lost rectus muscle based on knowledge of the orbital connective tissue framework.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".