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Record W1974653706 · doi:10.1136/rmdopen-2014-000007

A prospective study of the 1-year incidence of fibromyalgia after acute whiplash injury

2015· article· en· W1974653706 on OpenAlexaff
Robert Ferrari

Bibliographic record

VenueRMD Open · 2015
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineFibromyalgiaWhiplash injuryIncidence (geometry)WhiplashProspective cohort studyPhysical therapyInternal medicinePoison controlEmergency medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To measure the 1-year incidence of fibromyalgia in a cohort of acute whiplash-injured participants. METHODS: Consecutive acute patients with whiplash were assessed via the 2010 Modified American College of Rheumatology (ACR) criteria for fibromyalgia at 3 months, 6 months and 1 year postinjury. At each of these follow-up points, participants were also examined for recovery from whiplash injury. RESULTS: Of an initial 268 participants, data on recovery was available for 264 participants during the 1-year follow-up period. At the 3-month follow-up, 62% (167/268) of participants reported recovery from their whiplash injuries. At 6 months, 76% (203/268) reported recovery, and at 1 year 82% (216/264) reported recovery. At 3 and 6 months follow-up none of the participants met the 2010 Modified ACR Criteria for fibromyalgia, but fibromyalgia criteria were met for 2 (of 264) seen at the 1-year follow-up, yielding a 1-year incidence of 0.8% (95% CI 0.1% to 3.0%). CONCLUSIONS: In the primary care setting, a significant proportion of patients with whiplash recover from whiplash injury at 1 year, and the incidence of fibromyalgia after acute whiplash injury is very low. The impression that fibromyalgia is common after whiplash injury may be due to the failure to exclude precollision fibromyalgia cases or due to referral bias of non-recovered patients.

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 score0.008
Threshold uncertainty score0.318

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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.342
Teacher spread0.312 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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