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Record W2330730557 · doi:10.1097/btf.0000000000000088

The Treatment of Mueller-Weiss Disease: A Systematic Approach

2015· article· en· W2330730557 on OpenAlexaff
D. Joshua Mayich

Bibliographic record

VenueTechniques in Foot & Ankle Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsHorizon Health NetworkSaint John Regional HospitalDalhousie University
Fundersnot available
KeywordsMedicineModality (human–computer interaction)Treatment modalityPhysical medicine and rehabilitationSurgeryIntensive care medicineComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Mueller-Weiss disease (MWD), which involves dorsolateral fragmentation and collapse of the navicular, leads to functional misalignment and painful deformities. The successful treatment of MWD hinges on a detailed and through assessment of the patient to establish the modality of treatment that best suits the patient. In cases where operative management is indicated, proceeding to recreate a relatively pain-manageable (or where possible pain free), well-aligned, plantegrade foot is the goal. This can be performed technically by (1) determining as to which joints are involved/arthritic, and ensuring to address them; (2) establishing the amount of bone loss present, and planning to reconstitute this with graft material; (3) preparing the graft bed adequately and diligently to optimize the healing environment for the graft material; (4) providing a biomechanically sound treatment strategy that provides stability while the graft material heals; (5) and using orthopaedic principles while also remaining flexible. This is important because there is considerable variability in the anatomy and characteristics of MWD. Because of this, no single strategy is likely the “correct” method. Although the supporting literature remains sparse, when these principles are followed and postoperative complications can be avoided, significant improvements in function have been demonstrated and can be anticipated. Level of Evidence : Diagnostic Level 5. See Instructions for Authors for a complete description of levels of evidence.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.068
GPT teacher head0.302
Teacher spread0.234 · 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

Citations32
Published2015
Admission routes1
Has abstractyes

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