Characterization of baseline symptoms and functional impairments in a large cohort of outpatients attending a long covid rehabilitation clinic in the United Kingdom
Bibliographic record
Abstract
Objective: In response to the high prevalence and morbidity associated with long COVID (LC), outpatient rehabilitation programmes were created across jurisdictions. We aimed to characterize baseline symptoms and impairments of patients attending outpatient LC rehabilitation. Design: This study was a retrospective quality-improvement analysis. Subjects/Patients: Patients attending outpatient LC rehabilitation at the Oxfordshire Post-Covid Service. Methods: Data included age/sex and 6 questionnaires performed at baseline: Functional Assessment of Chronic Illness Therapy-Fatigue (FACIT-F), Dyspnoea-12 (D12), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder Assessment-7 (GAD-7), Visual Analogue Scale (VAS) of self-rated health, and the Work And Social Adjustment Scale (WSAS). All scores were dichotomized (indicating presence/absence of clinically significant pathology). Potential differences between age (</≥ 50 years) and sex were assessed using χ2 tests. Results: A total of 422 patients were included (mean/standard deviation [SD] age = 47.1/13.2;132/31.3% male). A total of 76% had significant fatigue (FACIT-F), 69% had breathlessness (D12), 55% had depression (PHQ-9), 34% had anxiety (GAD-7), 41% self-reported poor health (VAS), and 57% had work/social life dysfunction (WSAS). D12 scores differed between age groups (older > younger, χ2 = 3.19/p = 0.048), with no differences observed on other scales. Conclusion: In this preliminary study, a high proportion of LC outpatients had significant impairments across domains. The findings of this study reaffirm the need for high-quality, multidisciplinary LC rehabilitation, and may be used to help build a standardized set of outcome measures moving forward.
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".