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Record W2913993682 · doi:10.2319/080518-570.1

A randomized, single-blind, placebo-controlled trial to evaluate the effectiveness of verbal behavior modification and acetaminophen on orthodontic pain

2019· article· en· W2913993682 on OpenAlexaff
Harsimrat Kaur, Naveen K. Bansal, R. R. Abraham

Bibliographic record

VenueThe Angle Orthodontist · 2019
Typearticle
Languageen
FieldDentistry
TopicOrthodontics and Dentofacial Orthopedics
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAcetaminophenPlaceboMedicineVisual analogue scaleAnesthesiaRandomized controlled trialPlacebo groupSignificant differencePhysical therapyInternal medicine

Abstract

fetched live from OpenAlex

ABSTRACT Objectives: To evaluate the effectiveness of verbal behavior modification, acetaminophen, and the combined effectiveness of verbal behavior modification along with acetaminophen on orthodontic pain. Materials and Methods: One hundred and forty orthodontic fixed appliance patients were randomly assigned to four groups. Group A was administered acetaminophen, group B was given verbal behavior modification, group C was administered acetaminophen as well as verbal behavior modification, and group D was placebo-controlled. A visual analog scale was used to assess pain intensity after 1 week of separator placement. Results: Group A had less mean pain intensity when compared to group B at 6 hours ( P < .001) and at 1 ( P < .001) and 2 ( P = .002) days. Group C patients encountered less mean pain intensity when compared to group B patients at 6 hours ( P < .001) and at 1 ( P < .001), 2 ( P < .001), and 4 ( P = .001) days. There was a statistically significant difference between groups A and C (group C experienced less pain intensity) after 6 hours ( P = .004) and at day 4 ( P = .009) after separator placement. Conclusions: Acetaminophen is the main agent of orthodontic pain reduction after separator placement, with verbal behavior serving as an adjunct to it.

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)0.025
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0250.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.043
GPT teacher head0.319
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 teacher head, not a consensus.

Study designRandomized trial
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

Citations18
Published2019
Admission routes1
Has abstractyes

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