Advances in surgical approaches to the upper facial skeleton
Bibliographic record
Abstract
PURPOSE OF REVIEW: Surgical approaches to the upper facial skeleton comprise the coronal, lower eyelid and midface degloving approaches. These are routinely employed in both ablative and reconstructive craniofacial procedures. The ability to perform them in a well tolerated and predictable manner is predicated on knowledge of the indications and the exposure afforded by each approach, detailed appreciation of the anatomy and awareness of potential complications. This article reviews the literature for recent advancements and surgical refinements for each approach. RECENT FINDINGS: Multiple studies over the past 20 years have offered insight into many technical refinements in these surgical approaches. The choice of dissection plane in the lateral extension of the coronal approach affects the integrity of the frontal branch of the facial nerve and the temporal fat pad. A transcaruncular extension of the transconjunctival approach provides unprecedented access to the medial orbital wall and the midface degloving approach renders complex reconstructive procedures feasible. SUMMARY: These techniques continue to evolve and become more precise so that better results can be achieved and devastating complications can be avoided. This study reviews the literature and summarizes preferred options for craniofacial exposure, recent technical refinements, and our current preferred surgical approaches.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.006 | 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".