Current Management of Late Posttraumatic Enophthalmos
Bibliographic record
Abstract
LEARNING OBJECTIVES: After studying this article, the participant should be able to: 1. Describe the orbital anatomy and completely understand the important components relevant to surgical correction of enophthalmos, including oculo-orbito relations. 2. Understand the pathophysiology and predictive factors for posttraumatic enophthalmos and identify the challenges associated with correction of enophthalmos in the late setting. 3. Develop a surgical plan for late enophthalmos repair and understand the value and utility of osteotomies, intraoperative navigation, and patient-specific implants. 4. Discuss the expected outcomes, possible complications, and adjunctive surgery as related to late enophthalmos repair. SUMMARY: This article addresses the current management of late posttraumatic enophthalmos. In this article, the authors describe surgically relevant orbital anatomy and oculo-orbital relations, the pathophysiology of enophthalmos, clinical and radiologic findings, decision-making in management, and surgical treatment. The authors attempt to cover some of the main challenges and recent advances in the management of late posttraumatic enophthalmos, including intraoperative navigation and patient-specific implants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".