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Record W2285150269

Somatic, affective, and pain characteristics of chronic TMD patients with sexual versus physical abuse histories.

2000· article· en· W2285150269 on OpenAlexaboutno aff
Letitia Campbell, Joseph L. Riley, Susmita Kashikar‐Zuck, Henry A. Gremillion, Michelle Robinson

Bibliographic record

VenuePubMed · 2000
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsSexual abuseClinical psychologyPhysical abusePsychiatryBeck Depression InventoryChronic painPsychological abuseAnxietyPsychologyDistressMedicinePoison controlSuicide prevention
DOInot available

Abstract

fetched live from OpenAlex

AIMS: This study examined whether temporomandibular disorder (TMD) patients with sexual versus physical abuse histories differ in their pain report, psychological distress, and somatic symptoms. METHODS: Participants were 114 female TMD patients. The sample was divided into 3 groups based on abuse history: sexual abuse, physical abuse, or no abuse. Abuse histories were assessed with a structured clinical interview. Measures used included the McGill Pain Questionnaire, the State-Trait Anxiety Inventory, the Beck Depression Inventory, and the Pennebaker Inventory of Limbic Languidness. Group differences were analyzed by analysis of variance and Bonferroni post hoc comparisons. RESULTS: Temporomandibular disorder patients with a history of physical abuse reported significantly more pain, anxiety, and depressive symptoms than did patients with a history of sexual abuse or no history of abuse. Furthermore, the results suggest that TMD patients with a sexual abuse history are not significantly different from patients with no abuse history across the domains studied. CONCLUSION: Based on the differences found, it can be argued that assessment of physical abuse histories by appropriately trained clinicians should be a routine part of any multimodal assessment of female chronic TMD 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 score0.352
Threshold uncertainty score0.546

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 science0.0000.000
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 head0.015
GPT teacher head0.266
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 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

Citations40
Published2000
Admission routes1
Has abstractyes

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