MétaCan
Menu
Back to cohort

Childhood Maltreatment and Migraine (Part III). Association With Comorbid Pain Conditions

2009· article· en· W2129888118 on OpenAlexaffabout
Gretchen E. Tietjen, Jan Lewis Brandes, B. Lee Peterlin, Arnolda Eloff, Rima M. Dafer, Michael R. Stein, Ellen Drexler, Vincent T. Martin, Susan Hutchinson, Sheena K. Aurora, Ana Recober, Nabeel Herial, Christine Utley, Leah White, Sadik Khuder

Bibliographic record

VenueHeadache The Journal of Head and Face Pain · 2009
Typearticle
Languageen
FieldMedicine
TopicMigraine and Headache Studies
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMigraineAssociation (psychology)MedicinePsychiatryClinical psychologyPsychologyPsychotherapist

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate in a headache clinic population the relationship of childhood maltreatment on the prevalence of pain conditions comorbid with migraine. BACKGROUND: Childhood maltreatment is highly prevalent and has been frequently associated with recurrent headache. The relationship of maltreatment and pain has, however, been a subject of some debate. METHODS: Cross-sectional data on self-reported physician-diagnosed pain conditions were electronically collected from persons with migraine (diagnosed according to International Classification of Headache Disorders-2), seeking treatment in headache clinics at 11 centers across the US and Canada. These included irritable bowel syndrome (IBS), chronic fatigue syndrome (CFS), fibromyalgia (FM), interstitial cystitis (IC), arthritis, endometriosis, and uterine fibroids. Other information included demographics, migraine characteristics (frequency, headache-related disability), remote and current depression (The Patient Health Questionnaire-9), and remote and current anxiety (The Beck Anxiety Inventory). Patients also completed the Childhood Trauma Questionnaire regarding sexual, emotional, and physical abuse, and emotional and physical neglect under the age of 18 years old. Statistical analyses accounted for the survey design and appropriate procedures in SAS such as surveymeans, surveyfreq, and surveylogistic were applied to the weighted data. RESULTS: A total of 1348 migraineurs (88% women) were included in this study (mean age 41 years). Based on physician diagnosis or validated criteria, 31% had IBS, 16% had CFS, and 10% had FM. Diagnosis of IC was reported by 6.5%, arthritis by 25%, and in women, endometriosis was reported by 15% and uterine fibroids by 14%. At least 1 comorbid pain condition was reported by 61%, 2 conditions by 18%, and 3 or more by 13%. Childhood maltreatment was reported by 58% of the patients. Emotional abuse was associated with increased prevalence of IBS, CFS, arthritis, and physical neglect with arthritis. In women, physical abuse was associated with endometriosis and physical neglect with uterine fibroids. Emotional abuse, and physical abuse and neglect (P < .0001 for all) were also associated with increased total number of comorbid conditions. In ordinal logistic regression models, adjusted for sociodemographics and current depression (prevalence 28%) and anxiety (prevalence 56%), emotional abuse (odds ratios [OR] = 1.69, 95% confidence intervals [CI]: 1.224-2.33) and physical neglect (OR = 1.73, 95% CI: 1.22-2.46) were independently associated with an increased number of pain conditions. The cohort of women, similarly, had associations of emotional abuse (OR = 1.94, 95% CI: 1.40-2.72) and physical neglect (OR = 1.90, 95% CI: 1.34-2.68) with an increased number of pain comorbidities. CONCLUSION: The association of childhood maltreatment and pain was stronger in those reporting multiple pain conditions and multiple maltreatment types. This finding suggests that in migraineurs childhood maltreatment may be a risk factor for development of comorbid pain disorders.

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)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.013
GPT teacher head0.265
Teacher spread0.251 · 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.

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

Citations116
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueHeadache The Journal of Head and Face PainSame topicMigraine and Headache StudiesFrench-language works237,207