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Record W2049978535 · doi:10.1136/jcp.2006.042754

Definitions and aetiology of myalgic encephalomyelitis: how the Canadian consensus clinical definition of myalgic encephalomyelitis works

2006· review· en· W2049978535 on OpenAlexaffabout
Bruce M. Carruthers

Bibliographic record

VenueJournal of Clinical Pathology · 2006
Typereview
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsTelus (Canada)
Fundersnot available
KeywordsEncephalomyelitisEtiologyMedicinePerspective (graphical)PsychologyIntensive care medicinePsychiatryComputer scienceArtificial intelligenceMultiple sclerosis

Abstract

fetched live from OpenAlex

A perspective on the various definitions of myalgic encephalomyelitis and the process of discovering its aetiology is presented. The importance of clinical guidelines is emphasised to encourage clinicians to provide clear descriptions of their individual patients required for proper clinical activity; diagnosis, estimation of severity of effect, prognosis, treatment and rehabilitation. This individual knowledge is informed by general and (hopefully) publicly confirmed knowledge resulting from scientific research during the second-person interaction which lies at the core of the clinical encounter. Both types of knowledge are essential.

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 imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.717
Threshold uncertainty score0.562

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0110.011
Science and technology studies0.0020.008
Scholarly communication0.0060.006
Open science0.0040.003
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.264
GPT teacher head0.439
Teacher spread0.175 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations102
Published2006
Admission routes2
Has abstractyes

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