Prevention and Treatment of Influenza and COVID by Thermotherapy
Bibliographic record
Abstract
Objective: Influenza and COVID have killed millions of people. Allopathic or western medicine and Big Pharma use vaccines to prevent diseases caused by viruses. Over the years, humans have experienced several side effects of vaccines which are overlooked by the vaccine producers and world governments. These governments are controlled by allopathic medical practitioners and Big Pharma. Other medical remedies that cannot be patented to bring trillions of profits are neglected by Big Pharma and allopathic medical practitioners who see diseases as a cash cow. Vaccines are ineffective. By the time the vaccine is manufactured, the viruses have mutated making vaccines ineffective and sometimes lethal. COVID vaccines are ineffective, with several side effects which are sometimes lethal. It is rather unfortunate that Big Pharma and allopathic medical practitioners have dehumanized governments with their quackeries about vaccines. Big Pharma and allopathic medical practitioners will use whatever means, including death, to suppress other proven medical information pertaining to the prevention and treatment of Flu and COVID. One should check the growing mountain of insurance claims due to death and other deformities and illness as the result of the COVID vaccines in USA and Great Britain to learn how dangerous the vaccines are. Our research and clinical practice indicate that viruses, including influenza and COVID, can be prevented and cured by heat. Methodology: Prevention and Treatment of Influenza and COVID Using Steam Bath. The clinic for the study was located in Edmonton, Alberta, Canada is shown in Figure 1. The weather in Edmonton is cold throughout the year except a brief period of summer (Table 1) [1]. As viruses survive in colder temperature, Edmonton is a perfect location to study and treat diseases caused by viruses. The Steam Bath Studio was open from 10 am to 11 pm, 7 days a week. On the average 20 people attended the studio daily or approximately 7,200 people per year. Some of the attendees were perfectly healthy and others were suffering from influenza or COVID. The duration of staying in the steam bath was 30 to 60 minutes. Results and Conclusion: Over 14,000 patients with Flu or COVID were treated at the Steam Bath Studio in Edmonton over a period of two years. Heat kills all viruses including FLU and COVID. It has been proven scientifically and medically that viruses are killed by heat; hence diseases that are caused by viruses, such as Flu and COVID can be prevented and cured by thermotherapy. Vaccines are ineffective and have several side effects which could be lethal and shall not be used to prevent viral diseases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".