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Record W71099061 · doi:10.1155/2014/742830

Adjudication of Fibromyalgia Syndrome: Challenges in the Medicolegal Arena

2014· article· en· W71099061 on OpenAlexaffabout
Mary‐Ann Fitzcharles, Peter A. Ste‐Marie, Angela Mailis, Yoram Shir

Bibliographic record

VenuePain Research and Management · 2014
Typearticle
Languageen
FieldMedicine
TopicFibromyalgia and Chronic Fatigue Syndrome Research
Canadian institutionsUniversity of TorontoMcGill University Health Centre
Fundersnot available
KeywordsAdjudicationAttributionFibromyalgiaPsychosocialCausationCausality (physics)PsychologySubjectivityPsychiatryMedicineSocial psychologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

The medicolegal challenges surrounding fibromyalgia (FM) arise from the subjectivity of symptoms, causal attribution and reported symptoms sufficiently severe to cause disablement. In the present article, the authors have endeavoured to provide clarification of some current issues by referencing the current literature, including the 2012 Canadian Fibromyalgia Guidelines. While FM is accepted as a valid condition, its diagnosis is vulnerable to misuse due to the subjectivity of symptoms. Without a defining cause, a physical or psychological event may be alleged to trigger FM, but adjudication of causation must be prudent. Although some individuals may experience severe symptoms, the prevalent societal concept of disablement due to FM must be tempered with the knowledge that working contributes to psychosocial wellbeing. Evidence provided in the present report may assist the courts in reaching decisions concerning FM.

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)0.385
metaresearch head score (Gemma)0.521
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.385
Threshold uncertainty score0.758

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3850.521
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0060.004
Science and technology studies0.0080.020
Scholarly communication0.0100.007
Open science0.0060.008
Research integrity0.0100.012
Insufficient payload (model declined to judge)0.0020.001

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 head0.082
GPT teacher head0.349
Teacher spread0.267 · 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.

Study designNot applicable
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

Citations10
Published2014
Admission routes2
Has abstractyes

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