Correlation Between Prakriti (Body Constitution) and Severity of Structural Alterations in the Lungs of Patients With SARS-CoV-2: Protocol for a Retrospective Cross-Sectional Study
Bibliographic record
Abstract
Background: SARS-CoV-2, a novel coronavirus, initially appeared in Wuhan, China, at the end of 2019 and has infected more than 31 million people worldwide. Infection can range from asymptomatic to multiorgan failure requiring prompt treatment. Overall, 80% of patients with SARS-CoV-2 experienced mild to moderate illness, while 5% developed serious illness. Physicians can evaluate a patient's digestive system (Koshtha), digestive capacity (Agni), strength (Bala), and longevity (Ayu) using the body constitution (Deha Prakriti). It also helps doctors estimate a patient's illness risk, severity, disease activity scores, and hematological, pathological, and biochemical changes. The study investigated the association between body composition (Deha Prakriti) and severity, as shown by structural lung abnormalities in patients with SARS-CoV-2. Objective: This study aims to study the correlation between the severity of Prakriti and structural alterations in the lungs of patients with SARS-CoV-2. Methods: This is a retrospective cross-sectional study of patients with SARS-CoV-2. The research data will be retrospectively collected from hospital records between September 1, 2020, and May 11, 2021, a period during which India experienced the second wave of the pandemic. The data will come from the Acharya Vinoba Bhave Rural Hospital in Sawangi (Meghe), Wardha, Maharashtra, India. Patients will be contacted via telephone and encouraged to visit the outpatient department and inpatient department at these institutions or in rural and urban areas of Wardha city. A structured case pro forma and a Prakriti assessment questionnaire will be used to evaluate lung structural changes during the COVID-19-positive period. Results: The study is not funded by any organization. The study was initiated on March 4, 2023, and as of July 1, 2024, a total of 265 patients have been recruited. Results will be recorded from the observations of subjective and objective parameters. The study's primary outcome is to establish a relationship between abnormalities in the structure of the lungs in patients with COVID-19 and body constitution (Prakriti). The study's secondary outcome will help identify which body constitution is most susceptible to structural changes and disease severity in patients with COVID-19 and will also offer insights into preventive medicine. Conclusions: Statistical investigation will lead to the conclusion that there is a specific association between structural abnormalities in the lungs of patients with COVID-19 and their body constitution. We hypothesize that Prakriti will be identified as being more prone to structural changes and severity in patients with COVID-19, offering insights into preventive therapy.
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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 | 0.008 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".