Maxillary bone grafts for the repair of traumatic orbital floor defects.
Bibliographic record
Abstract
PURPOSE: To present maxillary bone (MB) grafts as a viable option for repair of traumatic orbital floor (TOF) defects by comparing their use to titanium mesh (TM) looking at TOF defect size, operative time, and complication rate. METHODS: The senior author's surgical technique is described. Patients undergoing TOF repair using MB versus TM were assessed retrospectively, focusing on TOF defect size, operative time, and follow-up results. RESULTS: One hundred ninety-six patients with 212 TOF defects presented to a single surgeon between 2004 and 2008. One hundred sixty-five patients (178 TOF defects) were repaired with MB and 31 patients (34 TOF defects) with TM. The MB and TM groups were similar with respect to age, gender, time to repair, and other associated facial fractures. TOF defect size was similar between the two groups (MB: mean 1.7 cm2, range 0.32-2.82 cm2; TM: mean 1.9 cm2, range 0.5-2.83 cm2). Follow-up was slightly longer in the TM group; however, many patients were lost to follow-up. There were no donor-site complications in the MB group and no significant difference in postoperative complications in the MB group versus the TM group (11% vs 24%). The operative time in patients with TOF defects was slightly longer in the MB group versus the TM group (35 min vs 27 minutes, p = .02). CONCLUSIONS: This series is the largest published series to date. MB was used successfully to repair TOF defects, with no increased risk of complications and only a slightly longer operative time compared to TM. MB offers an autogenous bone graft source that is technically easy to harvest and in the same surgical field, obviating many of the complications associated with alloplastic materials and traditional bone graft choices. MB grafts should be considered a viable option when choosing material to repair TOF defects.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".