Unraveling the Mystery Surrounding Post-Acute Sequelae of COVID-19
Bibliographic record
Abstract
More than one year since its emergence, corona virus disease 2019 (COVID-19) is still looming large with a paucity of treatment options. To add to this burden, a sizeable subset of patients who have recovered from acute COVID-19 infection have reported lingering symptoms, leading to significant disability and impairment of their daily life activities. These patients are considered to suffer from what has been termed as "chronic" or "long" COVID-19 or a form of post-acute sequelae of COVID-19, and patients experiencing this syndrome have been termed COVID-19 long-haulers. Despite recovery from infection, the persistence of atypical chronic symptoms, including extreme fatigue, shortness of breath, joint pains, brain fogs, anxiety and depression, that could last for months implies an underlying disease pathology that persist beyond the acute presentation of the disease. As opposed to the direct effects of the virus itself, the immune response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is believed to be largely responsible for the appearance of these lasting symptoms, possibly through facilitating an ongoing inflammatory process. In this review, we hypothesize potential immunological mechanisms underlying these persistent and prolonged effects, and describe the multi-organ long-term manifestations of COVID-19.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".