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Record W2011545725 · doi:10.11607/jop.1151

Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) for Clinical and Research Applications: Recommendations of the International RDC/TMD Consortium Network* and Orofacial Pain Special Interest Group†

2014· article· en· W2011545725 on OpenAlexafffund
Eric Schiffman, Richard Ohrbach, Edmond L. Truelove, John O. Look, Gary Clayton Anderson, Thomas List, Peter Svensson, Yoly González, Frank Lobbezoo, Ambra Michelotti, Sharon L. Brooks, Werner Ceusters, Mark Drangsholt, Dominik A. Ettlin, Charly Gaul, Louis J. Goldberg, Jennifer A. Haythornthwaite, L Hollender, William Maixner, Marylee van der Meulen, Greg M. Murray, Donald R. Nixdorf, Sandro Palla, Arne Petersson, Paul Pionchon, Barry Smith, Corine M. Visscher, Joanna M. Zakrzewska, Samuel F. Dworkin

Bibliographic record

VenueJournal of Oral & Facial Pain and Headache · 2014
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsUniversité Laval
FundersNational Human Genome Research InstituteNational Institute of Dental and Craniofacial ResearchCanadian Institutes of Health ResearchNational Institutes of Health
KeywordsOrofacial painResearch Diagnostic CriteriaTemporomandibular disorderMedicineFacial painPhysical therapyPhysical medicine and rehabilitationMedical physicsOrthodonticsChronic painTemporomandibular jointSurgery

Abstract

fetched live from OpenAlex

Eric Schiffman, DDS, MS/Richard Ohrbach, DDS, PhD/Edmond Truelove, DDS, MSD/John Look, DDS, PhD/Gary Anderson, DDS, MS/Jean-Paul Goulet, DDS, MSD/Thomas List, DDS, Odont Dr/Peter Svensson, DDS, PhD, Dr Odont/Yoly Gonzalez, DDS, MS, MPH/Frank Lobbezoo, DDS, PhD/Ambra Michelotti, DDS/Sharon L. Brooks, DDS, MS/Werner Ceusters, MD/Mark Drangsholt, DDS, PhD/Dominik Ettlin, MD, DDS/Charly Gaul, MD/Louis J. Goldberg, DDS, PhD/Jennifer A. Haythornthwaite, PhD/Lars Hollender, DDS, Odont Dr/Rigmor Jensen, MD, PhD/Mike T. John, DDS, PhD/Antoon De Laat, DDS, PhD/Reny de Leeuw, DDS, PhD/William Maixner, DDS, PhD/Marylee van der Meulen, PhD/Greg M. Murray, MDS, PhD/Donald R. Nixdorf, DDS, MS/Sandro Palla, Dr Med Dent/Arne Petersson, DDS, Odont Dr/Paul Pionchon, DDS, PhD/Barry Smith, PhD/Corine M. Visscher, PT, PhD/Joanna Zakrzewska, MD, FDSRCSI/Samuel F. Dworkin, DDS, PhD: Aims: The original Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) Axis I diagnostic algorithms have been demonstrated to be reliable. However, the Validation Project determined that the RDC/TMD Axis I validity was below the target sensitivity of ≥ 0.70 and specificity of ≥ 0.95. Consequently, these empirical results supported the development of revised RDC/TMD Axis I diagnostic algorithms that were subsequently demonstrated to be valid for the most common pain-related TMD and for one temporomandibular joint (TMJ) intra-articular disorder. The original RDC/TMD Axis II instruments were shown to be both reliable and valid. Working from these findings and revisions, two international consensus workshops were convened, from which recommendations were obtained for the finalization of new Axis I diagnostic algorithms and new Axis II instruments. Methods: Through a series of workshops and symposia, a panel of clinical and basic science pain experts modified the revised RDC/TMD Axis I algorithms by using comprehensive searches of published TMD diagnostic literature followed by review and consensus via a formal structured process. The panel’s recommendations for further revision of the Axis I diagnostic algorithms were assessed for validity by using the Validation Project’s data set, and for reliability by using newly collected data from the ongoing TMJ Impact Project—the follow-up study to the Validation Project. New Axis II instruments were identified through a comprehensive search of the literature providing valid instruments that, relative to the RDC/TMD, are shorter in length, are available in the public domain, and currently are being used in medical settings. Results: The newly recommended Diagnostic Criteria for TMD (DC/TMD) Axis I protocol includes both a valid screener for detecting any pain-related TMD as well as valid diagnostic criteria for differentiating the most common pain-related TMD (sensitivity ≥ 0.86, specificity ≥ 0.98) and for one intra-articular disorder (sensitivity of 0.80 and specificity of 0.97). Diagnostic criteria for other common intra-articular disorders lack adequate validity for clinical diagnoses but can be used for screening purposes. Inter-examiner reliability for the clinical assessment associated with the validated DC/TMD criteria for pain-related TMD is excellent (kappa ≥ 0.85). Finally, a comprehensive classification system that includes both the common and less common TMD is also presented. The Axis II protocol retains selected original RDC/TMD screening instruments augmented with new instruments to assess jaw function as well as behavioral and additional psychosocial factors. The Axis II protocol is divided into screening and comprehensive selfreport instrument sets. The screening instruments’ 41 questions assess pain intensity, pain-related disability, psychological distress, jaw functional limitations, and parafunctional behaviors, and a pain drawing is used to assess locations of pain. The comprehensive instruments, composed of 81 questions, assess in further detail jaw functional limitations and psychological distress as well as additional constructs of anxiety and presence of comorbid pain conditions. Conclusion: The recommended evidence-based new DC/TMD protocol is appropriate for use in both clinical and research settings. More comprehensive instruments augment short and simple screening instruments for Axis I and Axis II. These validated instruments allow for identification of patients with a range of simple to complex TMD presentations. J Oral Facial Pain Headache 2014;28:6–27. doi: 10.11607/jop.1151

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)0.030
metaresearch head score (Gemma)0.061
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.054
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.061
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.007
Bibliometrics0.0160.009
Science and technology studies0.0040.003
Scholarly communication0.0040.004
Open science0.0080.006
Research integrity0.0070.011
Insufficient payload (model declined to judge)0.0100.010

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 head0.169
GPT teacher head0.493
Teacher spread0.325 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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".

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Citations4,378
Published2014
Admission routes2
Has abstractyes

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