Conceptualising Long COVID as an episodic health condition
Bibliographic record
Abstract
### Summary box Globally growing numbers of individuals are living with persistent signs and symptoms following infection consistent with COVID-19, referred to as Long COVID or Post-COVID Conditions. An estimated 6.2% of the U K population self-identified experiencing Long COVID, which negatively affected their general well-being and ability to exercise and work.1 Long COVID symptoms also adversely affected day-to-day activities among 64.7% of those self-reporting Long COVID, with 19.3% reporting significant limitations to daily activities.2 The long-term trajectory of Long COVID remains unknown. Therefore, conceptualising disability in Long COVID is essential for better understanding the lived experiences and health-related challenges of people living with and affected by Long COVID, to inform effective rehabilitation approaches and interventions to enhance clinical practice, policy and research. Our aim is to conceptualise Long COVID as an episodic health condition resulting in disability that may be characterised as multidimensional, episodic and unpredictable in nature and to highlight future …
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".