MétaCan
Menu
Back to cohort
Record W2417100397

The effect of systemic administration of sclerostin antibody in a mouse model of distraction osteogenesis.

2014· article· en· W2417100397 on OpenAlexaff
Asim M. Makhdom, Frank Rauch, Dominique Lauzier, Reggie C. Hamdy

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsShriners Hospitals for Children - Canada
Fundersnot available
KeywordsSclerostinFibrocartilageMedicineDistraction osteogenesisSalineBone healingRegeneration (biology)Bone mineralSystemic administrationUrologyInternal medicinePathologyDistractionSurgeryChemistryOsteoporosisIn vivoBiology
DOInot available

Abstract

fetched live from OpenAlex

Distraction osteogenesis (DO) is a successful technique for bone lengthening, but one problem is the need to keep an external fixator in place until bone completely regenerates. We hypothesized that the systemic administration of sclerostin antibodies (Scl-Ab) can accelerate bone regeneration in a mouse model of DO. A total of 110 mice were randomized to receive one intravenous injection per week of either Scl-Ab (100 mg per kg body weight) or saline after DO surgery. Mice were sacrificed on day 11, 17, 34 or 51 post-surgery. Microcomputed tomography showed that bone volume per tissue volume of the Scl-Ab treated group was significantly higher on day 11 (P=0.009). Histological examinations indicated that chondrocytes and fibrocartilage predominated in the Scl-Ab group at day 11. The radiographic score of bone healing was also higher in Scl-Ab treated animals at day 11. There was a trend towards higher ultimate force and work to failure in Scl-Ab treated groups on day 34 and 51 (P>0.05). These data suggest the potential utility of Scl-Ab to reduce the time during DO when an external fixator is required.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
Threshold uncertainty score0.171

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 head0.014
GPT teacher head0.256
Teacher spread0.242 · 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

Citations17
Published2014
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicBone fractures and treatmentsFrench-language works237,207