Nasolacrimal Obstruction Following the Placement of Maxillofacial Hardware
Bibliographic record
Abstract
PURPOSE: This article reviews cases of nasolacrimal obstruction (NLO) secondary to maxillofacial hardware placement. METHODS: A retrospective review was performed at a single institution from 2012 to 2017 of patients with NLO following maxillofacial reconstruction. The study was approved by the Institutional Review Board of the University of California, San Francisco, adhered to the tenets of the Declaration of Helsinki, and was Health Insurance Portability and Accountability Act compliant. Patients were included if external dacryocystorhinostomy (DCR) confirmed previously placed maxillofacial hardware as the primary contributor to lacrimal outflow obstruction and had at least 3 months of follow-up. RESULTS: Of 420 patients who underwent external DCR, 6 cases of implant-related NLO were identified. The mean age was 47.3 ± 9.6 years and 66.7% of patients were male. All patients presented with epiphora and 50% also had chronic dacryocystitis. Patients had prior maxillofacial hardware placement for paranasal sinus tumors (66.7%) or facial fractures (33.3%). In addition to external DCR, all patients had revision or removal of implants that were impeding lacrimal outflow by 2 mechanisms: (1) an orbital implant impinging the lacrimal sac or nasolacrimal duct (NLD) and/or (2) maxillofacial screws placed into the bony NLD or nasolacrimal fossa. Five of the 6 patients (83.3%) had complete resolution of symptoms and patency of the nasolacrimal system at their last follow-up visit (range 3-30 months). CONCLUSION: NLO secondary to hardware placement, though infrequent, is underreported. Two mechanisms of hardware-induced NLO were encountered in this case series. Specific attention to nasolacrimal anatomy at the time of maxillofacial reconstruction may help minimize implant-induced NLO.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".