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Record W3208882908

Microbiologic characteristics of periimplantitis and periodontitis

2020· article· en· W3208882908 on OpenAlexaboutno aff
Diana Valentina Pérez Arenas, Jazbleydi Pérez Avendaño

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsPeriodontitisMedicineDentistry
DOInot available

Abstract

fetched live from OpenAlex

A systematic literature review is presented to clarify differences between the group of microbiologic characteristics of periimplantitis and periodontitis. According to the PRISMA declaration, searches in databases were carried out (PubMed, EBSCOhost, LILACS, Web Of Science and Clinical Key) and original works and systematic reviews were selected where comparing or analyzing microbiologic data obtained from samples of subgingival biofilms of patients with periodontitis and periimplantitis. The weight of evidence was evaluated by means of the Newcastle-Ottawa and PRISMA scales. Of the 335 identified works, 12 were included, of which 9 were observational studies and 3 systematic reviews. It was obtained that the microbiologic characteristics associated with periimplantitis are similar to those of periodontitis because they share a percentage of their microbiota, as the case of the periodontopathogen agents; however, bacterias only related to the periimplantar line were found. Finally, it is remarkable that in periimplantitis are bacterias that are mostly gramnegative anaerobias, periodontopathogens, opportunists and noncultivable; that is to say that their microbiologic characteristics are complex and differ from the specific characteristics of periodontitis.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.006
Bibliometrics0.0350.026
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.130
GPT teacher head0.482
Teacher spread0.352 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2020
Admission routes1
Has abstractyes

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