Our Experience of Trauma Management During Novel Coronovirus 2019 (COVID-19) Pandemic in a Busy Trauma Center in Southern Iran.
Bibliographic record
Abstract
During the past few months, the novel coronavirus 2019 (COVID-19) pandemic has significantly affected medical service provision. In Iran, it has caused around 197,000 inflictions and 9200 deaths up to June 18, 2020. While many departments turned to telehealth in this era, the trauma service should provide non-stop in presence service to the trauma victims. Our trauma center is the largest in the southwest of Iran, with the mean annual admission of 18,500 polytrauma patients. In this center, we designed a safety protocol to mitigate the spread of disease and also have a more robust case finding system, especially among asymptomatic carriers who attend hospitals based on their trauma. In brief, all unstable patients were considered SARS-COV-2 positive and were directed toward the Specialized COVID-19 related ICU. For all stable patients, history, physical examination, CXR, and lab test (Complete Blood Count, Erythrocyte Sedimentation Rate, C-Reactive Protein) were ordered before entering the wards. If there was any suspicion of COVID-19, the stable patient was admitted to the COVID-19 specialized ward. Among all 1805 patients admitted during a ten weeks interval (from January 30, 2020, to April 14, 2020), 84 had a red flag and toward to COVID-19 related wards. Of those, 67 had positive PCR or evidence in CT in favor of the COOVID-19. Moreover, during regular workups, we found that 19 completely asymptomatic trauma victims had typical Chest CT scan findings of COVID-19.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".