Patients diagnosed with Myalgic encephalomyelitis/chronic fatigue syndrome also fit systemic exertion intolerance disease criteria
Bibliographic record
Abstract
Background: Myalgic encephalomyelitis/ chronic fatigue syndrome (ME/CFS) remains undiagnosed in up to 91% of patients. Recently, the United States-based Institute of Medicine (IOM) developed new diagnostic criteria, naming it systemic exertion intolerance disease (SEID).Purpose: We examined how subjects fit SEID criteria and existing ME/CFS case definitions early in their illness.Methods: A total of 131 subjects fitting 1994 Fukuda CFS criteria at the time of study recruitment completed a survey of symptoms they experienced during their first 6 months of illness. Symptoms were drawn from SEID and existing criteria (1994 Fukuda, 2003 Canadian Consensus Criteria (CCC), and 2011 Myalgic Encephalomyelitis-International Consensus Criteria (ME-ICC)). We calculated and compared the number/percentage of subjects fitting single or combinations of case definitions and the number/percentage of subjects with SEID experiencing orthostatic intolerance (OI) and/or cognitive impairment.Results: At 6 months of illness, SEID criteria identified 72% of all subjects, similar to when Fukuda criteria (79%) or the CCC (71%) were used, whereas the ME-ICC selected for a significantly lower percentage (61%, p < .001). When severity/frequency thresholds were added to the Fukuda criteria, CCC and ME-ICC, the percentage of these subjects also fitting SEID criteria increased to 93%, 97%, and 95%. Eighty-seven percent of SEID subjects endorsed cognitive impairment and 92%, OI; 79% experienced both symptoms.Conclusions: SEID criteria categorize a similar percentage of subjects as Fukuda criteria early in the course of ME/CFS and contain the majority of subjects identified using other criteria while requiring fewer symptoms. The advantage of SEID may be in its ease of use.
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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".