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Record W4411126674 · doi:10.1177/24730114251341900

Posterior Malleolus: Morphologic Classification, Morphometry, and Clinical Insights

2025· article· en· W4411126674 on OpenAlexaff
Hellen Carvalho Ribeiro, William Paganini Mayer, Jacob Matz, Josemberg da Silva Baptista

Bibliographic record

VenueFoot & Ankle Orthopaedics · 2025
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsCanada East Spine CentreHorizon Health NetworkSaint John Regional Hospital
Fundersnot available
KeywordsAnatomyMedicineMorphometricsArticular surfaceTibiaFixation (population genetics)Tarsal BoneMedial malleolusAnkleBiology

Abstract

fetched live from OpenAlex

Background: In this study, we provide a comprehensive description of the morphometrics of the distal tibiae and propose that the intact posterior malleolus (PM) exhibits clinically relevant morphologic variation. These differences may have implications for fracture classification, fixation strategy, and implant design. Methods: Fifty-two isolated dry tibias were analyzed to determine the PM morphometric parameters. Five key morphometric points were identified, and the PM was defined as the posterior bony projection of the distal tibial epiphysis. The malleolar groove established the PM's medial limitation, the posterior portion of the fibular notch defined the lateral limit, and the anterior boundary was a line connecting these landmarks across the inferior articular surface. PM shapes were categorized based on consistent morphologic patterns. Cross-sections of the distal tibia were performed to assess trabecular bone morphology and density. Results: We found the PM presenting 3 distinct morphologic types: rounded, triangular, and trapezoid. Triangular and trapezoid types exhibited larger dimensions and robust bone tissue, whereas tibias with a rounded PM displayed smaller dimensions and delicate bone architecture. Conclusion: These novel findings reveal PM morphologic diversity, which may enhance our understanding of PM fracture patterns and optimize the development of surgical implants.

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 score0.083
Threshold uncertainty score0.678

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 head0.032
GPT teacher head0.334
Teacher spread0.302 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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