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Record W6921298067 · doi:10.6084/m9.figshare.c.5883850

Ligamentous injury-induced ankle instability causing posttraumatic osteoarthritis in a mouse model

2022· other· en· W6921298067 on OpenAlexaff

Bibliographic record

VenueFigshare · 2022
Typeother
Languageen
FieldArts and Humanities
TopicLibraries and Information Services
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsAnterior talofibular ligamentAnkleOsteoarthritisBalance (ability)InstabilityLigamentArthropathy

Abstract

fetched live from OpenAlex

Abstract Background This study aims to explore the relationship between surgically-induced ankle instability and posttraumatic osteoarthritis (PTOA) in a mouse model, and to provide reference for clinical practice. Results Ligamentectomy was performed on 24 eight-week-old male C57BL/6 J mice, which were divided into three groups. Both the anterior talofibular ligament (ATFL) and the calcaneofibular ligament (CFL) were severed in the CFL + ATFL group, while only the CFL was removed in the CFL group. The SHAM group was set as the blank control group. A wheel-running device was used to accelerate the development of ankle osteoarthritis (OA). Balance measurement, footprint analysis, and histological analysis were used to assess the degree of ankle instability and OA. According to the balance test results, the CFL + ATFL group demonstrated the highest number of slips and the longest crossing beam time at 8 weeks postoperatively. The results of gait analysis exhibited that the CFL + ATFL group had the most significant asymmetry in stride length, stance length, and foot base width compared to the CFL and SHAM groups. The OARSI score of the CFL + ATFL group (16.7 ± 2.18) was also much higher than those of the CFL group (5.1 ± 0.96) and the SHAM group (1.6 ± 1.14). Conclusion Based on the mouse model, the findings indicate that severe ankle instability has nearly three times the chance to develop into ankle OA compared to moderate ankle instability.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.850
Threshold uncertainty score1.000

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.8500.001

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 head0.069
GPT teacher head0.246
Teacher spread0.177 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2022
Admission routes1
Has abstractyes

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