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Record W2949414987 · doi:10.5114/jos.2018.85569

The psychological background of masticatory system parafunctions

2018· article· en· W2949414987 on OpenAlexaboutno aff
Tamara Pawlaczyk−Kamieńska

Bibliographic record

VenueJournal of Stomatology · 2018
Typearticle
Languageen
FieldArts and Humanities
Topiclinguistics and terminology studies
Canadian institutionsnot available
Fundersnot available
KeywordsMasticatory forceMedicineDentistry

Abstract

fetched live from OpenAlex

In recent years there has been a growth noted in the number of people suffering the effects of the masticatory organ motoric disorders which arise as a consequence of parafunctions.Some of them accompany mental disorders where practising parafunctions may ease anxiety and stress.Unfortunately, these parafunctions may produce injuries in the mouth.Moreover, they strain the motor system of the masticatory organ unevenly.This may lead to formation and fixation of a deviated model of function and, with time, to morphological and functional changes.Identification and elimination of a causative factor is essential in undertaking proper and effective treatment of these disorders.Moreover, it is necessary to promote parafunctions prophylaxis and to provide comprehensive, team-based treatment.It seems purposeful to implement comprehensive therapy at an early stage of the problem as a part of psychologic treatment, too.It should aim at eliminating harmful habits resulting in secondary somatic complications.The paper presents current views on etiology of masticatory organ parafunctions.

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)0.002
metaresearch head score (Gemma)0.009
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 score0.016
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0160.002

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 head0.070
GPT teacher head0.302
Teacher spread0.232 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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