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Record W2146531265 · doi:10.2106/jbjs.k.01344

Variability in the Definition and Perceived Causes of Delayed Unions and Nonunions

2012· article· en· W2146531265 on OpenAlexaff
Mohit Bhandari, Katie Fong, Sheila Sprague, Dale Williams, Bradley Petrisor

Bibliographic record

VenueJournal of Bone and Joint Surgery · 2012
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineNonunionRespondentOrthopedic surgeryDemographicsSurgeryPhysical therapyDemography

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the large number of fracture outcome studies, there remains variability in the definitions of fracture-healing. It is unclear how orthopaedic surgeons are diagnosing and managing delayed unions and nonunions in clinical practice. We aimed to explore the current opinions of orthopaedic surgeons with regard to defining, diagnosing, and treating delayed unions and nonunions in extremity fractures. METHODS: We developed a survey using previous literature, key informants in the field of orthopaedic surgery, and a sample-to-redundancy strategy. Our final survey contained four sections and twenty-nine questions focusing on demographics and surgical experience, definitions of fracture union, prognostic factors for union, and the need for clinical trials. The Internet-based survey and follow-up e-mails were continued until our a priori sample size of a minimum of 320 completed and eligible responses were collected. RESULTS: Three hundred and thirty-five surgeons completed the survey. The typical respondent was a North American, male orthopaedic surgeon or consultant over the age of thirty years who had completed trauma fellowship training, worked in an academic practice, supervised residents, and had more than six years of experience in treating orthopaedic injuries. Most surgeons endorsed a lack of standardization in definitions for delayed unions (73%) and nonunions (55%); almost all agreed that defining a delayed union and nonunion should be done on the basis of both radiographic and clinical criteria (88%). Most respondents believed that the degree of soft-tissue injury (approximately 93%), smoking history (approximately 82%), and vascular disease (approximately 76%) increased the risk of healing complications. CONCLUSIONS: Surgeons use similar prognostic factors to define and assess delayed unions and nonunions, but there is a lack of consensus in the definitions of delayed union and nonunion. The need for standardization and future randomized trials was strongly endorsed.

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)0.030
metaresearch head score (Gemma)0.119
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.030
Threshold uncertainty score0.156

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.119
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.046
GPT teacher head0.270
Teacher spread0.224 · 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 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

Citations176
Published2012
Admission routes1
Has abstractyes

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