MétaCan
Menu
← Back to cohort
Record W2557710647

VALIDATION OF A NOVEL CLASSIFICATION SYSTEM FOR DISTAL ULNAR FRACTURES ASSOCIATED WITH DISTAL RADIAL FRACTURES

2018· article· en· W2557710647 on OpenAlexaffabout
Laura Sims, Geoffrey Johnston, Samuel A. Stewart

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsSaskatoon Medical Imaging
Fundersnot available
KeywordsUlnaMedicineRadiographyOrthopedic surgeryRadial fracturesOrthodonticsAnatomyWristSurgery
DOInot available

Abstract

fetched live from OpenAlex

Distal ulna fractures (DUF) are commonly associated with distal radius fractures (DRF). Recent evidence suggests that the presence and type DUFs may impact the outcomes of associated healing distal radius fractures. There is currently no standardised and validated classification system for characterising distal ulna fractures. The purpose of this study was to assess the validity of our newly created inclusive classification system for distal ulna fractures, shown to influence distal radius fracture outcomes in a previous study. A classification system for distal ulna fractures was devised based on fracture pattern and location. Type 1 fractures were those in the ulnar styloid, with type 1a involving its apex and Type 1b being in the body of the styloid; Type 2 fractures are proximal to the styloid and involve the ulnar fovea, with type 2t adopting a transverse pattern and type 2o an oblique pattern; Type 3 fractures involve the ulnar head; and type 4 fractures were those proximal to the head, with 4n being through the neck (including the physeal scar) and 4s involving the distal shaft. A questionnaire was distributed to all members of the Canadian Orthopaedics Association in both French and English, asking participants to evaluate 29 radiographic images of distal ulnar fractures. Only one answer was deemed to be correct for all but one radiograph, while for one radiograph there were three fracture types to be identified. There were 129 respondents to the questionnaire. For Type 1a fractures, of the 606 radiographs evaluated 90% answered correctly and 73% of the incorrect answers identified a Type 1b fracture pattern. For Type 1b fractures, of 600 radiographs, 83% were answered correctly, the incorrect answers including Types 1a and 2t fracture types. For Type 2t fractures, of 593 radiographs, 76% were answered correctly, and 90% of the incorrect answers identified a Type 1b fracture pattern. For Type 2o fractures, of 716 radiographs, 87% were answered correctly, and 91% of the incorrect answers were identified as either Type 4n or 2t. For Type 4n fractures, of the 465 radiographs evaluated 84% answered correctly and 80% of the incorrect answers identified a Type 4s fracture pattern. For Type 4s fractures, of the 355 radiographs evaluated 99% answered correctly and 100% of the incorrect answers identified a Type 4n fracture pattern. The results will guide the authors to further distinguish between the definitions of Types 1b and 2t, and 4n and 4s. The Canadian orthopaedic community has demonstrated how readily they can reproduce this new classification system, previously shown to be predictive of radiographic outcomes for the associated distal radius fractures. This new classification is an inclusive and simple way of characterising these fractures with high reliability. This provides treating physicians with a uniform way of describing these fractures, useful both in predicting outcomes and conducting future research.

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)0.027
metaresearch head score (Gemma)0.070
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: Methods · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.145

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.070
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.275
Teacher spread0.233 · 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
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

Citations0
Published2018
Admission routes2
Has abstractyes

Explore more

Same topicHermeneutics and Narrative Identity→French-language works237,207→