A review of Patient Reported Outcome Measures (PROMs) for characterizing Long COVID (LC)—merits, gaps, and recommendations
Bibliographic record
Abstract
BACKGROUND: Individuals may experience a range of symptoms after the clearance of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. This condition is termed long COVID (LC) or Post-COVID-19 condition (PCC). Despite the appreciable number of symptoms documented to date, one key challenge remains in the robust characterization of LC outcomes. This review aimed to assess the properties, identify gaps, and provide recommendations for relevant descriptive and evaluative Patient-Reported Outcome Measurement (PROM) instruments that can be used to comprehensively characterize LC. METHODS: To achieve this objective, we identified and reviewed descriptive and evaluative PROM instruments that have been developed and validated to date with people living with LC. Our review assessed their properties, identified gaps, and recommended PROMs suitable for characterizing LC. To ensure a comprehensive and robust characterization of LC, we next identified, reviewed, and selected (with the input of patient partners) PROMs associated with the most frequently reported LC symptoms. The evaluation criteria included psychometric evidence, mode of delivery, cost, and administration time. RESULTS: Traditional matrix mapping revealed Post-COVID Functional Status Scale (PCFS) as a choice instrument for capturing LC outcomes largely because of the comprehensive domains it covered, and the number of psychometric evidence reported in literatures. This instrument can be effectively paired with the Fatigue Severity Scale (FSS), Montreal Cognitive Assessment (MoCA), Patient Health Questionnaire (PHQ-9), Headache Impact Test (HIT), Pittsburgh Sleep Quality Index (PSQI), and DePaul Symptom Questionnaire (DSQ-PEM) to characterize fatigue, cognitive impairment, depression/anxiety, headache, sleeplessness, and post-exertional malaise respectively. CONCLUSION: Our paper identified appropriate PROM instruments that can effectively capture the diverse impacts of LC. By utilizing these validated instruments, we can better understand and manage LC.
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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 | 0.065 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".