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Record W2016731078 · doi:10.1155/2013/365635

Health Technology Assessment Fireside: Antibiotic Prophylaxis and Dental Treatment in Canada

2012· review· en· W2016731078 on OpenAlexaffabout
Mario Brondani

Bibliographic record

VenueJournal of Pharmaceutics · 2012
Typereview
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineGuidelineAntibiotic prophylaxisIntensive care medicineContext (archaeology)PopulationPsychological interventionAntibioticsEnvironmental healthNursing

Abstract

fetched live from OpenAlex

Objectives. This paper discusses the controversies surrounding the antibiotic prophylaxis preceding dental interventions within the following research question: how effective is dental antibiotic prophylaxis in preventing comorbidity and complications in those at risk? Methods. A synthesis of the available literature regarding antibiotic prophylaxis in dentistry was conducted under the lenses of Kazanjian's framework for health technology assessment with a focus on economic concerns, population impact, social context, population at risk, and the effectiveness of the evidence to support its use. Results. The papers reviewed show that we have been using antibiotic prophylaxis without a clear and full understanding of its benefits. Although the first guideline for antibiotic prophylaxis was introduced in 1990, it has been revised on several occasions, from 1991 to 2011. Evidence-based clinical guidelines are yet to be seen. Conclusions. Any perceived potential benefit from administering antibiotic prophylaxis before dental procedures must be weighed against the known risks of lethal toxicity, allergy, and development, selection, and transmission of microbial resistance. The implications of guideline changes and lack of evidence for the full use of antibiotic prophylaxis for the teaching of dentistry have to be further discussed.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.004
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 head0.698
GPT teacher head0.642
Teacher spread0.056 · 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 designNot applicable
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

Citations3
Published2012
Admission routes2
Has abstractyes

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