Silent sinus syndrome after facial trauma: A case report and literature review
Bibliographic record
Abstract
OBJECTIVE: The accepted definition of silent sinus syndrome (SSS) excludes posttraumatic cases. To challenge current exclusion criteria of antecedent facial trauma, we have identified all published cases of posttraumatic SSS in English literature, including a new representative case from our institution. DATA SOURCES: MEDLINE, EMBASE, and Scopus databases. REVIEW METHODS: All case reports and case series published in English literature from 1964 through August 2016 were sequentially identified. Authors of cases with missing information were contacted for completion. RESULTS: Thirteen documented cases of posttraumatic SSS were identified through the literature review. An additional case from our institution was presented, bringing the total reported case count to 14. Time from initial trauma to presentation ranged from 2 months to 32 years, with a median duration of 6 months. Endoscopic sinus surgery (ESS) with either concurrent or staged orbital floor implant repair was used to treat posttraumatic SSS in 64% of reported cases. Three patients had ESS alone, with one case showing postoperative improvement in enophthalmos. CONCLUSION: Recent emergence of case reports of SSS postorbital and facial trauma challenge the current exclusion criteria of precedent facial trauma. Posttraumatic SSS is rare, but the availability of cross-sectional imaging pre- and postdevelopment of SSS makes a strong case for a causal relationship. Laryngoscope, 127:1520-1524, 2017.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".