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 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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".