A Low Dose Whole Lung Radiotherapy for Covid-19 Pneumonia: What have we Learned? Opinion of the International Geriatric Radiotherapy Group.
Bibliographic record
Abstract
Background: Coronavirus disease 19 carry a high mortality rate among older patients and minorities such as ethnic Africans and Latinos through the induction of a cytokines storm. Many pharmacologic interventions were proposed to improve the mortality rate from normal organ damage such as pneumonia. Low dose whole lung radiotherapy has been used in the past to treat pneumonia and may improve survival through modulation of the inflammatory cytokines. However, there is a lot of controversy about the efficacy and safety of this treatment modality. Thus, a review of the clinical studies using irradiation for COVID-19 pneumonia is needed to answer those questions Methods: A literature search of PubMed and Google Scholar was conducted. Reported studies were analyzed to assess safety, efficacy, and inflammatory biomarkers response following low dose whole lung radiotherapy Results: Patients who required artificial ventilation for COVID-19 did not benefit from low dose whole lung radiotherapy, most likely due to severe lung damage. The inflammatory response may be attenuated after irradiation but it is unclear whether it is independent of the steroid effect. Conclusion: Randomized studies are required to assess the effect low dose whole lung radiotherapy for COVID-19 pneumonia and its anti-inflammatory property. Such studies are needed for emerging countries with limited resources as radiotherapy may be cost-effective to reduce hospital admission and intensive care unit monitoring.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".