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Record W2656752099 · doi:10.1111/ocr.12151

Effect of exogenous retinoic acid on tooth movement and periodontium healing following tooth extraction in a rat model

2017· article· en· W2656752099 on OpenAlexaff
Clarice Nishio, Pierre Rompré, Florina Moldovan

Bibliographic record

VenueOrthodontics and Craniofacial Research · 2017
Typearticle
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsUniversité de Montréal
FundersAmerican Association of Orthodontists Foundation
KeywordsDental alveolusMedicinePeriodontiumIsotretinoinMolarDentistryBone healingAnatomy

Abstract

fetched live from OpenAlex

OBJECTIVES: To evaluate the effect of isotretinoin on orthodontic tooth movement (OTM) and wound healing following exodontia. SETTING AND SAMPLE POPULATION: Sixteen 40-day-old male Wistar rats were divided into two groups: (a) OTM and (b) tooth extraction (TE) of the upper 1st molar and OTM. The experimental animals were treated with isotretinoin (7.5 mg/kg) and the control animals with oil solution for 37 days. MATERIALS AND METHODS: The OTM and bone volume were evaluated by the micro-CT and the periodontium healing was assessed by immunohistochemistry for VEGF-C, COX-2 and IL-1ß. RESULTS: The animals of both groups submitted to the TE showed a statistically significant decrease in the bone volume percentage and increase in OTM. No significant difference of OTM and bone volume was observed between the control and experimental group. However, the alveolar bone of the isotretinoin group revealed more medullary spaces with inflammatory, hematopoietic cells, blood vessels and intense immunolabeling for VEGF-C. This group also showed faster gingival regeneration. No significant difference was observed in the COX-2 and IL-1ß labelings following TE between both groups. CONCLUSION: The isotretinoin did not affect the OTM nor did it cause an alteration in maxillary bone volume. This exogenous acid may contribute to the acceleration of gingival healing.

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)0.005
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.124
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.064
GPT teacher head0.409
Teacher spread0.345 · 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 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

Citations19
Published2017
Admission routes1
Has abstractyes

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