The association between borderline personality disorder, fibromyalgia and chronic fatigue syndrome: systematic review
Bibliographic record
Abstract
BACKGROUND: Overlap of aetiological factors and demographic characteristics with clinical observations of comorbidity has been documented in fibromyalgia syndrome, chronic fatigue syndrome (CFS) and borderline personality disorder (BPD). AIMS: The purpose of this study was to assess the association of BPD with fibromyalgia syndrome and CFS. The authors reviewed literature on the prevalence of BPD in patients with fibromyalgia or CFS and vice versa. METHODS: A search of five databases yielded six eligible studies. A hand search and contact with experts yielded two additional studies. We extracted information pertaining to study setting and design, demographic information, diagnostic criteria and prevalence. RESULTS: We did not identify any studies that specifically assessed the prevalence of fibromyalgia or CFS in patients with BPD. Three studies assessed the prevalence of BPD in fibromyalgia patients and reported prevalence of 1.0, 5.25 and 16.7%. Five studies assessed BPD in CFS patients and reported prevalence of 3.03, 1.8, 2.0, 6.5 and 17%. CONCLUSIONS: More research is required to clarify possible associations between BPD, fibromyalgia and CFS. DECLARATION OF INTEREST: None. COPYRIGHT AND USAGE: © The Royal College of Psychiatrists 2016. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".