Best Practice Guidelines for the Management of Acute Craniomaxillofacial Trauma During the COVID-19 Pandemic
Bibliographic record
Abstract
Coronavirus disease 2019 (COVID-19) is an infectious disease that is caused by severe respiratory syndrome coronavirus 2. Although elective surgical procedures are being cancelled in many parts of the world during the COVID-19 pandemic, acute craniomaxillofacial (CMF) trauma will continue to occur and will need to be appropriately managed. Surgical procedures involving the nasal, oral, or pharyngeal mucosa carry a high risk of transmission due to aerosolization of the virus which is known to be in high concentration in these areas. Intraoperative exposure to high viral loads through aerosolization carries a very high risk of transmission, and the severity of the disease contracted in this manner is worse than that transmitted through regular community transmission. This places surgeons operating in the CMF region at particularly high risk during the pandemic. There is currently a paucity of information to delineate the best practice for the management of acute CMF trauma during the COVID-19 pandemic. In particular, a clear protocol describing optimal screening, timing of intervention and choice of personal protective equipment, is needed. The authors have proposed an algorithm for management of CMF trauma during the COVID-19 pandemic to ensure that urgent and emergent CMF injuries are addressed appropriately while optimizing the safety of surgeons and other healthcare providers. The algorithm is based on available evidence at the time of writing. As the COVID-19 pandemic continues to evolve and more evidence and better testing becomes available, the algorithm should be modified accordingly.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".