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Amitriptyline treatment of chronic pain in patients with temporomandibular disorders

2000· article· en· W3026992446 on OpenAlexaboutno aff
Octavia Plesh, David Curtis, J. Levine, W.D. McCall

Bibliographic record

VenueJournal of Oral Rehabilitation · 2000
Typearticle
Languageen
FieldMedicine
TopicBotulinum Toxin and Related Neurological Disorders
Canadian institutionsnot available
Fundersnot available
KeywordsAmitriptylineMedicineVisual analogue scaleMcGill Pain QuestionnaireBeck Depression InventoryDepression (economics)Temporomandibular jointResearch Diagnostic CriteriaPhysical therapyClinical trialRandomized controlled trialTemporomandibular Joint DisorderChronic painAnesthesiaInternal medicineDentistryPsychiatry

Abstract

fetched live from OpenAlex

Randomized clinical trials of amitriptyline will require data from pilot studies to be used for sample size estimates, but such data are lacking. This study investigated the 6‐week and 1‐year effectiveness of low dose amitriptyline (10–30 mg) for the treatment of patients with chronic temporomandibular disorder (TMD) pain. Based on clinical examination, patients were divided into two groups: myofascial and mixed (myofascial and temporomandibular joint disorders). Baseline pain was assessed by a Visual Analogue Scale (VAS) for pain intensity and by the McGill Pain Questionnaire (MPQ). Depression was assessed by the Beck Depression Inventory (BDI) short form. Patient assessment of global treatment effectiveness was obtained after 6 weeks and 1 year of treatment by using a five‐point ordinal scale: (1) worse, (2) unchanged, (3) minimally improved, (4) moderately improved, (5) markedly improved. The results showed a significant reduction for all pain scores after 6 weeks and 1 year post‐treatment. The depression scores changed in depressed but not in non‐depressed patients. Global treatment effectiveness showed significant improvement 6 weeks and 1 year post‐treatment. However, pain and global treatment effectiveness were less improved at 1 year than at 6 weeks.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.318
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.005
GPT teacher head0.235
Teacher spread0.230 · 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

Citations27
Published2000
Admission routes1
Has abstractyes

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