Evolving Trends in the Management of Orbital Floor Fractures
Bibliographic record
Abstract
BACKGROUND: The management of orbital floor fractures is diverse and continues to evolve. The purpose of the current study was to provide an updated summary of the literature, with a focus on interspecialty differences, and contrast that with current treatment strategies of actively practicing plastic surgeons. METHODS: A survey was conducted of surgeons who currently manage orbital floor fractures. The results are summarized and compared with a 10-year literature review (2002-2012) of surgical approaches, indications and timing of surgery, and implant selection in various surgical disciplines. Inclusion criteria included studies in English language with 10 or more patients. RESULTS: The survey response rate was 56%, of which 86 surgeons were identified to currently manage orbit fractures. A third of participants reported they are less likely to operate on these fractures relative to earlier in their career. Six factors were found to have the greatest influence on surgeon's operative decision: enophthalmos, hypophthalmos, positive forced duction, defect size, motility restriction, and persistent diplopia. The most common preferred approach to the orbit is midlid/infraorbital (45%) followed by transconjunctival (31%) and subciliary (24%). Medpor and titanium are the most preferred implants (83%) compared with autologous bone (5%). CONCLUSIONS: Significant interdisciplinary and intradisciplinary differences in the management of orbital fractures exist. The most significant trends are the growing popularity of alloplastic versus autogenous materials for orbital floor reconstruction and the fact that one-third of surgeons are more likely to opt for a nonoperative (conservative) approach compared with earlier in their careers.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".