The new criteria for a COVID19 patient for the clinical practice to determine the need for an early therapeutic regimen and to decrease mortality
Bibliographic record
Abstract
A new predictive criterion is being proposed for the determination of cytokine storm (CS) in COVID-19 (COVID-CS). It is comprised of results of laboratory that associate the pro-inflammatory status, systemic cell death, multi-organ tissue damage, and pre-renal electrolyte imbalance. The data identifies the patients’ stay in hospitals and their mortality with the relevance of hyper-inflammation and tissue damage during the CS. The criteria can be readily used in clinical practice to determine the need for an early therapeutic regimen, block the hyper-immune response and possibly decrease mortality. It helps to understand the nature of the virus by following a specific criterion to predict the disease. The SARS-CoV-2 tells us in few days what nature has decided for the patient i.e., recovery, death or permanent disability. doi: https://doi.org/10.12669/pjms.37.5.3630 How to cite this:Bukhari MH, Zain S, Syed M. The new criteria for a COVID19 patient for the clinical practice to determine the need for an early therapeutic regimen and to decrease mortality. Pak J Med Sci. 2021;37(5):1536-1539. doi: https://doi.org/10.12669/pjms.37.5.3630 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".