Review of COVID-19 Re-Infection among Recovered Patients and Its Implication for Lung Health
Bibliographic record
Abstract
COVID-19 infection has continued to pose a very serious health threat to mankind globally despite all efforts geared toward curbing its spread. More worrisome recently is the report from different parts of the world on the re-infection of those treated and recovered with COVID -19 patients thus making containment of the virus even more difficult. Of more worrisome is the fact that the lung, a vital human organ is a major site being attacked by the virus even on re-infection cases. If quick action is not taken early enough, it may lead to the outright death of the patient. A lung infection, (Pneumonia) caused by COVID-19 has been discovered to be having a stunning effect on hospital systems and killing COVID-19 patients silently and it occurs even as the patient is asymptomatic. This paper examines the reasons for re-infection, Lacuna in the reviewed literature with regards to PCR test results, the effect of re-infection on the lungs, and implication for patients’ lung health. The papers summarized and concluded that it’s a fact that re-infection occurs among patients accompanied by mild or severe symptoms having far-reaching implications for the patient’s lung health. The paper recommends that the government at all levels should collaborate with WHO, CDC, and health policymakers to legally mandating, that every recovered patient should stay an additional 2weeks in the hospital for early detection of re-infection in order to avert any invasion and damage to the lungs thus ensuring lung health. Also, proper health education should be availed to the recovered patients to avoid any exposures or habits (different from the index disease) such as smoking that can pose dangers to the already fatigued lungs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.001 |
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".