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Record W2980331538

Fibromyalgia syndrome: under-, over- and misdiagnosis.

2019· article· en· W2980331538 on OpenAlexaff
Winfried Häuser, Piercarlo Sarzi‐Puttini, Mary‐Ann Fitzcharles

Bibliographic record

VenuePubMed · 2019
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsFibromyalgiaMedicineChronic painDifferential diagnosisPhysical therapyPhysical examinationMedical diagnosisChronic fatigue syndromeMedical historyIntensive care medicineInternal medicinePathology
DOInot available

Abstract

fetched live from OpenAlex

Fibromyalgia syndrome (FM) is an enigma. During the past three decades, with the gradual acceptance of the validity of FM, it is variously under-, over and misdiagnosed. Evidence-based interdisciplinary guidelines have suggested a comprehensive clinical assessment to avoid this diagnostic conundrum. Every patient with chronic pain should be screened for chronic widespread pain (pain in four of five body regions) (CWP). Those with CWP should be screened for presence of additional major symptoms of FM: unrefreshed sleep and fatigue. A complete medical (including drug) history and complete physical examination is mandatory in the evaluation of a patient with CWP in order to consolidate the diagnosis of FM or identify features that may point to some other condition that may have a presentation similar to FM. Limited simple laboratory testing is recommended to screen for possible other diseases. The 2016 criteria may be used to further confirm the clinical diagnosis of FM. In consideration of the differential diagnosis of FM, attention should be paid to the presence of other chronic overlapping pain conditions and of mental disorders. FM as a stand alone diagnosis is however rare, as most patients with FM meet criteria for other chronic overlapping pain conditions or mental disorders. The severity of FM should be assessed in order to direct treatment approaches and help inform the likely outcome for an individual patient.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.018
GPT teacher head0.251
Teacher spread0.233 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations160
Published2019
Admission routes1
Has abstractyes

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