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Record W2013813107 · doi:10.1093/rheumatology/keg075

Inaccuracy in the diagnosis of fibromyalgia syndrome: analysis of referrals

2003· article· en· W2013813107 on OpenAlexafffund
M.-A. Fitzcharles

Bibliographic record

VenueBritish journal of rheumatology · 2003
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsMcGill University Health CentreMontreal General Hospital
FundersCanadian Institutes of Health Research
KeywordsMedicineOverdiagnosisMorning stiffnessFibromyalgiaPhysical therapyInternal medicineDisease

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine prospectively the accuracy of an initial diagnosis for fibromyalgia (FM). METHODS: All patients newly referred for rheumatology consultation in a 6-month period were evaluated prospectively for either a preceding, current or subsequent diagnosis of FM. Clinical characteristics, previous and subsequent management and health care utilization were assessed. The final diagnosis at 6 months was verified and accuracy regarding the diagnosis of FM was assessed. RESULTS: Seventy six (12%) of all new patients were either referred with a question of FM or finally diagnosed with FM. At the final evaluation the accuracy of the diagnosis regarding FM by either the referring physician or by the rheumatologist at the time of the initial visit was correct in 34% of patients. The FM group in comparison with those with some other rheumatological diagnosis had more tender points (12.5 vs 4) and were more fatigued. In contrast, prolonged early morning stiffness and limitation of lumbar spinal mobility in more than one plane was more common in the non-FM group. CONCLUSION: There is a disturbing inaccuracy, mostly observed to be overdiagnosis, in the diagnosis of FM by referring physicians. This finding may help explain the current high reported rates of FM and caution physicians to consider other diagnostic possibilities when addressing diffuse musculoskeletal pain.

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)0.002
metaresearch head score (Gemma)0.002
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 score0.110
Threshold uncertainty score0.948

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.029
GPT teacher head0.315
Teacher spread0.286 · 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

Citations154
Published2003
Admission routes2
Has abstractyes

Explore more

Same venueBritish journal of rheumatologySame topicFibromyalgia and Chronic Fatigue Syndrome ResearchFrench-language works237,207