A proposed approach to pulmonary long COVID: a viewpoint
Bibliographic record
Abstract
Long COVID (also known as “post-acute sequelae of COVID-19”) is a multi-system disorder that follows an acute bout of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection [1]. Although its exact prevalence is unknown, it is estimated to affect approximately 10% of SARS-CoV-2 infected individuals, though in reality the percentage is likely much higher owing to under-reporting of cases [1]. The prevalence is elevated in patients who have had acute SARS-CoV-2 pneumonia requiring hospitalization and lower in those who have been previously vaccinated or were infected with the Omicron variant [1]. In approximately 6% of the cases of long COVID, pulmonary symptoms such as dyspnea, cough and wheeziness are prominent, leading to considerable disability and morbidity [2, 3]. While it is attractive to view long COVID as one disease, it is likely a very complex, heterogeneous disorder, with multiple different phenotypes, each driven by a unique set of molecules and pathways [1].
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.034 | 0.040 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".