A Mini-review about Concomitant Burn and COVID-19
Bibliographic record
Abstract
COVID-19 can cause different catastrophic events and mortalities. Therefore, burn hospitals strategies changed during regional peak of COVID-19 pandemic in the world. Some outpatient strategies were introduced to minimize the contact with infected patients. However, admission strategies were based on the severity of COVID-19 symptoms and also severity of burn injuries. The policy for admission to give inpatient care relates to history, symptoms and COVID-19 PCR results. If all the mentioned criteria were negative, the burn patient was admitted to GREEN area or ward. If the patient was suspected or positive for COVID-19, then would be admitted to RED area or ward. Given that some patients may be carriers without specific symptoms, considering a YELLOW area or ward seems logical for these groups. In GREEN areas one care giver for each adult and two care givers for each pediatric patient were allowed. For all care givers wearing medical masks were obligatory. All patients, care givers and staffs were under constant surveillance for fever and other symptoms. In RED areas no visitors were allowed and for each patient one nurse was assigned in BICU. All elective surgeries were stopped and other procedures were divided to emergency and semi-emergency. Strategies for facing COVID-19 surges especially new variants need to continuously evolve. Changing of infectivity rate, manifestations, resistance to different vaccine and duration of viral shedding necessitate modification of principles according to data collected from involving countries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
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".