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Record W2885472800 · doi:10.7717/peerj.5464

Osteogenesis imperfecta: potential therapeutic approaches

2018· article· en· W2885472800 on OpenAlexaff
Maxime Rousseau, Jean-Marc Retrouvey

Bibliographic record

VenuePeerJ · 2018
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicConnective tissue disorders research
Canadian institutionsMcGill University
Fundersnot available
KeywordsDentinogenesis imperfectaMedicineOsteogenesis imperfectaDentistryPermanent dentitionDentitionMultidisciplinary approachOrthodontics

Abstract

fetched live from OpenAlex

Osteogenesis imperfecta (OI) is a genetic disorder that is usually caused by disturbed production of collagen type I. Depending on its severity in the patient, this disorder may create difficulties and challenges for the dental practitioner. The goal of this article is to provide guidelines based on scientific evidence found in the current literature for practitioners who are or will be involved in the care of these patients. A prudent approach is recommended, as individuals affected by OI present with specific dentoalveolar problems that may prove very difficult to address. Recommended treatments for damaged/decayed teeth in the primary dentition are full-coverage restorations, including stainless steel crowns or zirconia crowns. Full-coverage restorations are also recommended in the permanent dentition. Intracoronal restorations should be avoided, as they promote structural tooth loss. Simple extractions can also be performed, but not immediately before or after intravenous bisphosphonate infusions. Clear aligners are a promising option for orthodontic treatment. In severe OI types, such as III or IV, orthognathic surgery is discouraged, despite the significant skeletal dysplasia present. Given the great variations in the severity of OI and the limited quantity of information available, the best treatment option relies heavily on the practitioner's preliminary examination and judgment. A multidisciplinary team approach is encouraged and favored in more severe cases, in order to optimize diagnosis and treatment.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.058
Threshold uncertainty score0.409

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.038
GPT teacher head0.289
Teacher spread0.251 · 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 designBench or experimental
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

Citations37
Published2018
Admission routes1
Has abstractyes

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