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Record W2790442716 · doi:10.1111/jcpe.12885

Investigation of factors that influence pain experienced and the use of pain medication following periodontal surgery

2018· article· en· W2790442716 on OpenAlexaff
Jennifer R. Beaudette, Péter Fritz, Philip Sullivan, Assunta Piccini, Wendy E. Ward

Bibliographic record

VenueJournal Of Clinical Periodontology · 2018
Typearticle
Languageen
FieldDentistry
TopicDental Anxiety and Anesthesia Techniques
Canadian institutionsBrock University
Fundersnot available
KeywordsMedicineSedationPillAnesthesiaVisual analogue scaleChronic painImplantSurgeryPhysical therapy

Abstract

fetched live from OpenAlex

AIMS: To determine the relationship between anticipated pain and actual pain experienced following soft tissue grafting or implant surgery; to identify the factors that predict actual pain experienced and the use of pain medication following soft tissue grafting or implant surgery. MATERIALS AND METHODS: Prior to dental implant placement (n = 98) or soft tissue grafting (n = 115) and for seven days following the procedure, patients completed a visual analog scale indicating anticipated or experienced pain, respectively. The use of pain medication and alcohol, and smoking were measured. RESULTS: Actual pain experienced on day 1 was lower (p < .01) than anticipated pain and continued to decrease (p ≤ .01) for each of the 7 consecutive days. Anticipated and actual pain were positively correlated. Increasing age (p < .05), having sedation during the surgery (p < .05), and lower use of pain pills (p < .01) predicted lower pain experienced. Actual pain experienced was a predictor of pain pill use (p < .01). Greater nervousness (p < .01) prior to surgery was a predictor of greater anticipated pain. CONCLUSIONS: Patients anticipated more pain than they actually experienced. Sedation, age and number of pain pills used predicted pain experienced. This trial was registered with clinicaltrials.gov as NCT03064178.

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)0.008
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.125
GPT teacher head0.359
Teacher spread0.234 · 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 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
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueJournal Of Clinical PeriodontologySame topicDental Anxiety and Anesthesia TechniquesFrench-language works237,207