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Record W4315640814 · doi:10.1177/00220345221139230

Acute Postoperative Pain Due to Dental Extraction in the Adult Population: A Systematic Review and Network Meta-analysis

2023· review· en· W4315640814 on OpenAlexaff
Anna Miroshnychenko, Sara Ibrahim, Maria Azab, Yetiani Roldán, Juan Pablo Díaz Martinez, Divyalakshmi Tamilselvan, Leon He, J.W. Little, Olivia Urquhart, Malavika P. Tampi, Deborah E. Polk, Paul A. Moore, Elliot V. Hersh, Brannon R. Claytor, Alonso Carrasco‐Labra, Romina Brignardello‐Petersen

Bibliographic record

VenueJournal of Dental Research · 2023
Typereview
Languageen
FieldDentistry
TopicDental Anxiety and Anesthesia Techniques
Canadian institutionsMcMaster UniversityImpact
FundersBayer HealthCareU.S. Food and Drug AdministrationUniversity of PennsylvaniaPfizerU.S. Department of Health and Human Services
KeywordsMeta-analysisDental extractionExtraction (chemistry)MedicineDentistryPopulationInternal medicineChemistryEnvironmental healthChromatography

Abstract

fetched live from OpenAlex

This study compares the effectiveness of pharmacological treatments to develop guidelines for the management of acute pain after tooth extraction. We searched Medline, EMBASE, CENTRAL, and US Clinical Trials registry on November 21, 2020. We included randomized clinical trials (RCTs) of participants undergoing dental extractions comparing 10 interventions, including acetaminophen, nonsteroidal anti-inflammatory drugs (NSAIDs), opioids, and combinations to placebo. After duplicate screening and data abstraction, we conducted a frequentist network meta-analysis for each outcome at 6 h (i.e., pain relief, total pain relief [TOTPAR], summed pain intensity difference [SPID], global efficacy rating, rescue analgesia, and adverse effects). We assessed the risk of bias using a modified Cochrane RoB 2.0 tool and the certainty of evidence using the Grading of Recommendations, Assessment, Development, and Evaluation approach. We implemented the analyses in RStudio version 3.5.3 and classified interventions from most to least beneficial or harmful. We included 82 RCTs. Fifty-six RCTs enrolling 9,095 participants found moderate- and high-certainty evidence that ibuprofen 200 to 400 mg plus acetaminophen 500 to 1,000 mg (mean difference compared to placebo [MDp], 1.68; 95% confidence interval [CI], 1.06-2.31), acetaminophen 650 mg plus oxycodone 10 mg (MDp, 1.19; 95% CI, 0.85-1.54), ibuprofen 400 mg (MDp, 1.31; 95% CI, 1.17-1.45), and naproxen 400-440 mg (MDp, 1.44; 95% CI, 1.07-1.80) were most effective for pain relief on a 0 to 4 scale. Oxycodone 5 mg, codeine 60 mg, and tramadol 37.5 mg plus acetaminophen 325 mg were no better than placebo. The results for TOTPAR, SPID, global efficacy rating, and rescue analgesia were similar. Based on low- and very low-certainty evidence, most interventions were classified as no more harmful than placebo for most adverse effects. Based on moderate- and high-certainty evidence, NSAIDs with or without acetaminophen result in better pain-related outcomes than opioids with or without acetaminophen (except acetaminophen 650 mg plus oxycodone 10 mg) or placebo.

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)0.016
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.461
Threshold uncertainty score0.863

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0160.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.214
GPT teacher head0.489
Teacher spread0.274 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations47
Published2023
Admission routes1
Has abstractyes

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