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Record W2589764312 · doi:10.1177/2309499017692703

Perceived risk factors for nonunion following foot and ankle arthrodesis

2017· article· en· W2589764312 on OpenAlexaff
Gowreeson Thevendran, Kalpesh Shah, Stephen J. Pinney, Alastair Younger

Bibliographic record

VenueJournal of orthopaedic surgery · 2017
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineNonunionArthrodesisAnkleFoot (prosody)SurgeryPhysical therapyFoot and ankle surgeryOrthopedic surgery

Abstract

fetched live from OpenAlex

BACKGROUND: A major complication of foot and ankle arthrodesis is nonunion, which occurs in approximately 12% of cases. Various factors influence a patient's risk for nonunion following foot and ankle arthrodesis. We surveyed international foot and ankle surgeons to determine (1) risk factors perceived most important for nonunion, (2) factors considered absolute contraindications for arthrodesis, and (3) differences among expert groups regarding perceived risk factors and their stratification. METHODS: A questionnaire was e-mailed to members of a major foot and ankle journal editorial board and four foot and ankle society executive committees. The relative risk of 18 potential nonunion risk factors was rated from 1 to 10, using smoking 1 pack/day as a benchmark score of 5.00. RESULTS: The response rate was 72% (100/139); 81% declared foot and ankle surgery encompasses >90% of their practice. The highest perceived risk factors ( p < 0.001) were smoking 2 packs/day (mean score 8.69), lack of fusion site stability (8.66), and poor local vascularity (7.66). The least important risk factors ( p < 0.001) were perceived to be age >60 years (mean score 2.54), rheumatoid arthritis (3.05), and osteoporosis (3.56). The most frequently cited absolute contraindications to arthrodesis surgery were local infection (46%), poor local vascularity (41%), and smoking (32%). CONCLUSION: To improve arthrodesis outcomes, resource allocation and patient and surgeon education should focus on smoking, construct stability, and local vascularity. Development of an objective nonunion risk assessment tool to identify patients at risk for nonunion using these results could help maximize the efficiency of available resources.

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)0.005
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 score0.038
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.042
GPT teacher head0.293
Teacher spread0.251 · 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

Citations46
Published2017
Admission routes1
Has abstractyes

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