Association between child maltreatment and central sensitivity syndromes: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: A growing body of evidence is identifying the link between a history of child maltreatment and a variety of adverse health outcomes ultimately leading to significant social and healthcare burden. Initial work has identified a potential association between child maltreatment and the development of a selection of somatic and visceral central sensitivity syndromes: fibromyalgia, chronic fatigue syndrome, temporomandibular joint disorder, chronic lower back pain, chronic neck pain, chronic pelvic pain, interstitial cystitis, vulvodynia, chronic prostatitis, tension-type headache, migraine, myofascial pain syndrome, irritable bowel syndrome and restless legs syndrome. METHODS AND ANALYSIS: Primary electronic searches will be performed in the Embase, MEDLINE, PubMed, Scopus, PyscINFO, CINAHL and Cochrane Library databases and a number of Grey Literature sources including child protection and paediatric conference proceedings. Following independent screening of studies by two review authors, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses template will be used to aid extraction. A meta-analysis will be conducted on the included case-control and cohort studies. The Newcastle-Ottawa grading system will be used to assess the quality of included studies. Results will be expressed as pooled ORs for binary data and mean differences for continuous data. ETHICS AND DISSEMINATION: Ethics approval will not be required. The final results of the review and meta-analysis will be submitted for peer-review publication and also disseminated at relevant conference presentations. PROSPERO REGISTRATION NUMBER: CRD42018089258.
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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 | 0.069 | 0.055 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.020 | 0.013 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.092 | 0.013 |
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".