Review of organ damage from COVID and Long COVID: a disease with a spectrum of pathology
Bibliographic record
Abstract
Long COVID, as currently defined by the World Health Organization (WHO) and other authorities, is a symptomatic condition that has been shown to affect an estimated 10 %-30 % of non-hospitalized patients after one infection. However, COVID-19 can also cause organ damage in individuals without symptoms, who would not fall under the current definition of Long COVID. This organ damage, whether symptomatic or not, can lead to various health impacts such as heart attacks and strokes. Given these observations, it is necessary to either expand the definition of Long COVID to include organ damage or recognize COVID-19-induced organ damage as a distinct condition affecting many symptomatic and asymptomatic individuals after COVID-19 infections. It is important to consider that many known adverse health outcomes, including heart conditions and cancers, can be asymptomatic until harm thresholds are reached. Many more medical conditions can be identified by testing than those that are recognized through reported symptoms. It is therefore important to similarly recognize that while Long COVID symptoms are associated with organ damage, there are many individuals that have organ damage without displaying recognized symptoms and to include this harm in the characterization of COVID-19 and in the monitoring of individuals after COVID-19 infections.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".