ME/CFS: Causes, Clinical Features and Diagnosis
Bibliographic record
Abstract
“ME/CFS: Causes, Clinical Features and Diagnosis” addresses the early stages of ME/CFS and underlying predisposing factors. It considers the plight of the individual patient, and also the impact of the illness on society as a whole, which is considerable, in terms of both costs and social disruption. Patients and their families and carers frequently experience discrimination and difficulties accessing care. This volume will be of particular interest to those undertaking scientific research and those providing clinical care for ME/CFS patients, as well as to social policy analysts, policy makers and governments, and specialists in social research and medical education. There is a major focus on shortcomings in terms of medical education, resulting in considerable gaps in knowledge and understanding of the condition among many doctors. International comparisons indicate that these problems are encountered in many countries. This is particularly topical at a time when Long Covid-19 has moved post-viral syndromes to the forefront of the political agenda, and confronted society with new challenges in this area on a hitherto unprecedented scale. The volume addresses the many points of similarity between Long Covid-19 and ME/CFS. Mitigation of the illness is also addressed, through espousal of a more patient-centred approach to care, and through consideration of the scope for prevention. Sixty-nine authors from seventeen European countries, and from Canada and the USA have contributed to this volume, which is a truly international collaboration.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".