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Record W2899448816 · doi:10.17567/ataunidfd.395391

TEMPOROMANDİBULAR EKLEM RAHATSIZLIKLARINDA OKLUZAL FAKTÖRLERİN DEĞERLENDİRİLMESİ

2018· article· tr· W2899448816 on OpenAlexaff
Neslihan Tınastepe, İpek İŞCAN

Bibliographic record

VenueAtatürk Üniversitesi Diş Hekimliği Fakültesi Dergisi · 2018
Typearticle
Languagetr
FieldSocial Sciences
TopicGender Studies and Social Issues
Canadian institutionsStantec (Canada)
Fundersnot available
KeywordsMedicineTemporomandibular jointGynecologyOrthodonticsDentistry

Abstract

fetched live from OpenAlex

Amaç: Bu araştırmanın amacı okluzal faktörler ile temporomandibular eklem (TME) rahatsızlıkların ilişkisinin incelenmesidir. Materyal ve Metod: Çene yüz ağrıları kliniğine art arda gelen hastaların kayıtları retrospektif olarak incelendi. TME rahatsızlıkları için araştırma tanı kriterleri (ATK/TME) kullanılarak TME rahatsızlığı tanısı konuldu. Angle sınıflaması, anterior ve lateral rehberlik faktörleri belirlendi. Ki- kare ve Continuity Yates düzeltmesi kullanılarak verilerin niteliksel karşılaştırması yapıldı. Bulgular: TME rahatsızlığı teşhisi konulan bireylerde kanin koruyuculu okluzal rehberliğine eklem rahatsızlığı bulunmayanlardan daha fazla oranda rastlanıldı. Myofasiyal ağrı teşhisi konulan bireylerde anterior rehberlik varlığı, disk deplasmanı teşhisi konulan bireylerde Angle Sınıf I molar ilişkisi, artraljisi bulunanlarda ise kanin koruyuculu okluzyon diğer alt gruplardan daha fazla oranda tespit edildi. Sonuç: Sonuç olarak seçilmiş okluzal faktörlerle temporomandibular eklem rahatsızlıkları arasında ilişki bulunmuştur.Temporomandibular eklem rahatsızlıkları değerlendirilirken bu faktörler göz önünde bulundurul- malıdır ancak temporomandibular rahatsızlıkların etyolojisinin multifaktöriyel olduğu unutulmamalıdır Anahtar Kelimeler: Okluzyon, Malokluzyon, Temporomandibular eklem EVALUATION OF OCCLUSAL FACTORS IN TEMPOROMANDIBULAR JOINT DISORDERS ABSTRACT Aim: The aim of the study was to investigate the relation between main occlusal factors and temporomandibular disorders. Material and Methods: Records of patients attending orofacial pain clinic were screened retrospectively.The diagnosis of temporomandibular disorders were made according to research diagnostic criteria for temporomandibular disorders (RDC/TMD). Angle classification, the type of occlusal guiding pattern were determined. Chi-square and Yates’s continuity correction were used to compare qualitative data. Results: Canine guided occlusion were found more frequently in subjects with temporomandibular disorders than in subjects without temporomandibular disorders. It was also observed that prevalance of anterior guidance in subjects with myofacial pain, Angle Class I molar relation in subjects with disc displacement, canine guidance occlusion in subjects with artraljia was higher than other subgroups of temporomandibular disorders. Conclusion: In conclusion, it was found that there is an association between selected occlusal factors and temporomandibular disorders. Meanwhile, multifac- torial etiology should be kept in mind while evaluating impact of occlusal factors on temporomandibular disorders. Keywords: Occlusion, Malocclusion, Temporo- mandibular joint.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.002

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 head0.023
GPT teacher head0.301
Teacher spread0.277 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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