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Record W4387472626 · doi:10.1177/19476035231205676

Evidence-Based Treatment Algorithm for Freiberg Disease

2023· article· en· W4387472626 on OpenAlexaff
Ichiro Yoshimura, Masato Takao, Emilio Wagner, Sjoerd A.S. Stufkens, Jari Dahmen, Gino M. M. J. Kerkhoffs, Mark Glazebrook

Bibliographic record

VenueCartilage · 2023
Typearticle
Languageen
FieldMedicine
TopicBone and Joint Diseases
Canadian institutionsQueen Elizabeth II Health Sciences Centre
Fundersnot available
KeywordsMedicineDebridement (dental)SurgeryOsteotomyArthroplastyDiseaseAvascular necrosisStage (stratigraphy)Femoral headPathologyBiology

Abstract

fetched live from OpenAlex

Freiberg disease is a type of osteonecrosis of the metatarsal head that predominantly occurs in young females and adolescents, although it may occur at any age. The pathophysiology is multifactorial and may involve trauma, altered foot biomechanics, systemic disorders, and arterial insufficiency. The most typical location is the second metatarsal head, but Freiberg disease may also occur in other lesser toes. Nonoperative treatment is best applied in the early stage of the disease; if this is ineffective, surgical treatment is recommended. Currently available surgical procedures include debridement, osteotomy, osteochondral grafting, microfracture, interposition arthroplasty, implant arthroplasty, and metatarsal shortening arthroplasty. In this article, we propose a treatment algorithm for Freiberg disease based on the current literature and expert opinion.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0070.002
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0120.003

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 head0.102
GPT teacher head0.331
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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

Citations14
Published2023
Admission routes1
Has abstractyes

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