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Record W2120774339 · doi:10.2174/1874210601307010036

Dietary Strategies to Optimize Wound Healing after Periodontal and Dental Implant Surgery: An Evidence-Based Review

2013· article· en· W2120774339 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Open Dentistry Journal · 2013
Typearticle
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsNiagara Health SystemBrock University
Fundersnot available
KeywordsMedicineDentistryMicronutrientWound healingPeriodontal surgeryOral healthIntensive care medicineSurgeryPathology

Abstract

fetched live from OpenAlex

Methods to optimize healing through dietary strategies present an attractive option for patients, such that healing from delicate oral surgeries occurs as optimally as possible with minimal patient-meditated complications through improper food choices. This review discusses findings from studies that have investigated the role of diet, either whole foods or individual dietary components, on periodontal health and their potential role in wound healing after periodontal surgery. To date, research in this area has largely focused on foods or individual dietary components that may attenuate inflammation or oxidant stress, or foster de novo bone formation. These studies suggest that a wide variety of dietary components, including macronutrients and micronutrients, are integral for optimal periodontal health and have the potential to accelerate oral wound healing after periodontal procedures. Moreover, this review provides guidance regarding dietary considerations that may help a patient achieve the best possible outcome after a periodontal procedure.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.094
GPT teacher head0.376
Teacher spread0.281 · 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