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Record W3129704916 · doi:10.21270/archi.v10i2.5201

Evaluation of the quality of life and the impact of pain in patients with temporomandibular disorder

2021· article· en· W3129704916 on OpenAlexaboutno aff
Claudio Marcio Rodrigues Santana, Victor Augusto Alves Bento, Edílson José Zafalon, Maria Cristina Mesquita, Daisilene Baena Castillo

Bibliographic record

VenueARCHIVES OF HEALTH INVESTIGATION · 2021
Typearticle
Languageen
FieldHealth Professions
TopicTemporomandibular Joint Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineQuality of life (healthcare)McGill Pain QuestionnairePhysical therapyTemporomandibular jointTemporomandibular disorderActivities of daily livingVisual analogue scaleDentistry

Abstract

fetched live from OpenAlex

Objective: To evaluate the quality of life and the impact of pain in patients with Temporomandibular Disorder (TMD) Muscle/joint TMJ of a public institution in Brazil. Methods: The study consisted of two stages: the first step was the application of the Ohip-14 questionnaires, Fonseca's Anamnestic Questionnaire and the McGill Questinionaire (Br-MPQ). In the next step, after the clinical treatment, only the Ohip-14 questionnaire was applied to compare the results after the treatment. The significance level was set at α = 0,05. Results: Overall, 100 individuals were examined and diagnosis with TMD Muscle/joint TMJ. The score OHIP-14 scale before the treatment was 30.02 ± 1.26 (mean ± standard error of the mean) points, being that after treatment it was 8.94 ± 0.63 points, statistically significant (p<0,001). The mean score in the Fonseca scale was 73.25 ± 1.93 points. Regarding the Br-MPQ, the results showed that TMD pain affects the patient in the area of work, leisure, home activities, family relationships, relationships with friends, sleep and appetite. Conclusion: SERDOF-DTM patients had severe TMD with a negative impact on quality of life, directly affecting their daily activities. The treatment proved to be effective in improving this condition.

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)0.010
metaresearch head score (Gemma)0.005
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 score0.015
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.062
GPT teacher head0.400
Teacher spread0.338 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueARCHIVES OF HEALTH INVESTIGATIONSame topicTemporomandibular Joint DisordersFrench-language works237,207