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Record W3112519033 · doi:10.3390/ani10122347

Does the Animal Model Influence in Vertical Alveolar Distraction? A Systematic Review of the Literature

2020· review· en· W3112519033 on OpenAlexaboutno aff
Mario García-González, Fernando Muñóz, Antonio González‐Cantalapiedra, Mónica López‐Peña, Nikola Saulačić

Bibliographic record

VenueAnimals · 2020
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCraniofacial Disorders and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAnimal modelDistractionMedicineSystematic reviewMeta-analysisScale (ratio)Quality of evidenceMEDLINEPsychologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

This study is aimed at synthesizing all available evidence on vertical alveolar osteogenesis distraction (VAOD) in animal trials to determine whether the animal model used and its handling influence or not, and find which is the most appropriate animal model for this specific technique. This systematic review has been carried out following the PRISMA statements. Bibliographic sources have been consulted manually by two reviewers. Risk of bias was assessed using a version of the Newcastle-Ottawa-Scale (NOS). The selection criteria established by expert researchers were applied in order to decide which studies should be included in the review. Twenty-six studies met the inclusion criteria and were included in the review. Twenty-four of them had a high quality (score between 7 and 9), two medium quality (score between 4 and 6), and none low quality (score between 1 and 3). The highest possible score was 9 (using the NOS). Six studies complied with all NOS criteria. The animal model has been seen to influence the results, leading to failure in some cases. The most used animal model on VAOD, with fewer complications, was the Mongrel dog. The use of the pig and minipig is not recommended, due to the difficulties in handling and complications encountered.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.094
Threshold uncertainty score0.398

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.010
GPT teacher head0.305
Teacher spread0.294 · 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 designSystematic review
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

Citations2
Published2020
Admission routes1
Has abstractyes

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