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Record W4289517044 · doi:10.1007/s10006-022-01104-8

Effect of time-dependent ibuprofen administration on the post operatory after impacted third molar extraction: a cross-over randomized controlled trial

2022· article· en· W4289517044 on OpenAlexaff
Fabián Pérez‐González, Mohammad Abusamak, Luis Miguel Sáez-Alcaide, Jesús Torres, Faleh Tamimi

Bibliographic record

VenueOral and Maxillofacial Surgery · 2022
Typearticle
Languageen
FieldDentistry
TopicDental Radiography and Imaging
Canadian institutionsMcGill University
FundersUniversidad Complutense de Madrid
KeywordsIbuprofenMedicineTrismusMolarRandomized controlled trialAnesthesiaOral and maxillofacial surgeryVisual analogue scalePlaceboCrossover studySurgeryDentistry

Abstract

fetched live from OpenAlex

PURPOSE: To evaluate time-dependent administration of ibuprofen in a lower third molar extraction model. METHODS: Eleven patients requiring bilateral surgical removal of lower third molars were recruited and randomized into a blinded crossover randomized controlled trial. For 3 days after surgery, the control group was prescribed ibuprofen 400 mg every 8 h. On the other hand, the experimental group received also ibuprofen 400 mg at breakfast and lunch, replacing the dinner intake with a placebo. Pain measurements (Visual Analog Scale from 0 to 10) were recorded at baseline, 24, 48, and 72 h postoperatively. Facial swelling and trismus were also measured at baseline, 24, and 72 h postoperatively. RESULTS: Postoperative swelling and pain perception did not show significative difference between the control and experimental groups at 24, 48, and 72 h. Trismus was significantly lower in the control group than in the experimental group at 72 h postoperatively (p = 0.008). Rescue medication consumption seemed to be comparable between groups. CONCLUSION: Eliminating night time ibuprofen might be insignificant for pain control after third molar extraction.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.016
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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)0.0030.000

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 head0.007
GPT teacher head0.276
Teacher spread0.268 · 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

Citations8
Published2022
Admission routes1
Has abstractyes

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