The Facilitation of Clinical and Therapeutic Discoveries in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome and Related Diseases: Protocol for the You + ME Registry Research Platform
Bibliographic record
Abstract
BACKGROUND: Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic, complex, heterogeneous disease that affects millions and lacks both diagnostics and treatments. Big data, or the collection of vast quantities of data that can be mined for information, have transformed the understanding of many complex illnesses, such as cancer and multiple sclerosis, by dissecting heterogeneity, identifying subtypes, and enabling the development of personalized treatments. It is possible that big data can reveal the same for ME/CFS. OBJECTIVE: This study aims to describe the protocol for the You + ME Registry, present preliminary results related to participant enrollment and satisfaction, and discuss the limitations of the registry as well as next steps. METHODS: We developed and launched the You + ME Registry to collect longitudinal health data from people with ME/CFS, people with long COVID (LC), and control volunteers using rigorous protocols designed to harmonize with other groups collecting data from similar groups of people. RESULTS: As of September 30, 2021, the You + ME Registry had over 4200 geographically diverse participants (3033/4339, 69.9%, people with ME/CFS; 833/4339, 19.2%, post-COVID-19 people; and 473/4339, 10.9%, control volunteers), with an average of 72 new people registered every week. It has qualified as "great" using a net promotor score, indicating registrants are likely to recommend the registry to a friend. Analyses of collected data are currently underway, and preliminary findings are expected in the near future. CONCLUSIONS: The You + ME Registry is an invaluable resource because it integrates with a symptom-tracking app, as well as a biorepository, to provide a robust and rich data set that is available to qualified researchers. Accordingly, it facilitates collaboration that may ultimately uncover causes and help accelerate the development of therapies. TRIAL REGISTRATION: ClinicalTrials.gov NCT04806620; https://clinicaltrials.gov/ct2/show/NCT04806620. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/36798.
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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 | 0.141 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.079 | 0.030 |
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".