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Record W2586258458 · doi:10.1080/03008207.2017.1288725

Strategies of enhancing bone regenerate formation in distraction osteogenesis

2017· review· en· W2586258458 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

VenueConnective Tissue Research · 2017
Typereview
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsMcGill UniversityMontreal Children's Hospital
Fundersnot available
KeywordsDistractionDistraction osteogenesisMedicineModalitiesBone formationRegeneration (biology)Orthopedic surgeryProcess (computing)DentistryIntensive care medicineSurgeryComputer sciencePsychologyNeuroscienceInternal medicine

Abstract

fetched live from OpenAlex

Distraction osteogenesis (DO) is a commonly used technique in multiple orthopedic sub-specialties, including trauma, oncology and pediatrics. This technique aims to produce new bone formation in the distraction gap in a controlled manner. The issue with this technique has been the high risk of complications, one of which is poor regenerate formation during the distraction process. Although several factors (including patient and operative factors) and techniques (including surgical, mechanical and pharmacological) have been described to ensure successful regenerate formation during the process of DO, these factors are sometimes difficult to control clinically. Our aim from this review is to highlight the different factors that affect DO, modalities to assess the regenerate and review treatment options for poor regenerate in the distraction gap. In addition, we propose a management protocol derived from the available literature that can be used to facilitate the management of inadequate regenerate formation.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
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 head0.315
GPT teacher head0.535
Teacher spread0.220 · 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