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Record W4310710350 · doi:10.1521/bumc.2022.86.4.316

Adaptation of movement decoupling for compulsive joint cracking: A case report

2022· article· en· W4310710350 on OpenAlexaff
Steffen Moritz, Yves Bellinghausen, Stella Schmotz, Danielle Penney

Bibliographic record

VenueBulletin of the Menninger Clinic · 2022
Typearticle
Languageen
FieldMedicine
TopicMusicians’ Health and Performance
Canadian institutionsCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-MontréalDouglas Mental Health University Institute
Fundersnot available
KeywordsDysfunctional familyFeelingPsychologyRandomized controlled trialCrackingDecoupling (probability)MedicinePhysical medicine and rehabilitationPsychiatrySocial psychologySurgeryEngineering

Abstract

fetched live from OpenAlex

Compulsive joint cracking is a body-focused repetitive behavior (BFRB), which often results in negative social feedback due to its characteristic sound. While behavioral techniques are recommended in BFRBs, no published studies or case reports exist specifically for compulsive joint cracking. The authors report the case of DZ, who engaged in severe joint cracking of his knuckles and, at times, his back. The individual was assessed with an adapted version of the Generic BFRB Scale (GBS). DZ was instructed on how to perform decoupling, a technique that has shown efficacy in other BFRBs. He was also advised to use "fidget devices" that mimic aspects of the dysfunctional behavior in a less conspicuous way. Scores on the GBS were reduced by almost 50%, which corresponded with DZ's subjective appraisal of feeling more in control. Randomized controlled trials are needed to assess the (differential) efficacy these techniques to ameliorate compulsive joint cracking.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0060.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.078
GPT teacher head0.340
Teacher spread0.263 · 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 designCase report
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

Citations2
Published2022
Admission routes1
Has abstractyes

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