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Record W2799314775 · doi:10.1097/bot.0000000000001179

Biomechanical Testing of a 3-Hole Versus a 4-Hole Sliding Hip Screw in the Presence of a Retrograde Intramedullary Nail for Ipsilateral Intertrochanteric and Femur Shaft Fractures

2018· article· en· W2799314775 on OpenAlexaff
Michael Olsen, Peter Goshulak, Meghan Crookshank, Joel Moktar, Ignace J. Brazda, Emil H. Schemitsch, Radovan Zdero

Bibliographic record

VenueJournal of Orthopaedic Trauma · 2018
Typearticle
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsWestern UniversityToronto Metropolitan UniversityVictoria HospitalUniversity of Toronto
FundersOrthopaedic Trauma Association
KeywordsIntramedullary rodMedicineCadaveric spasmFemurDynamic hip screwNail (fastener)Displacement (psychology)SurgeryOrthodonticsMaterials science

Abstract

fetched live from OpenAlex

OBJECTIVE: The goal of this study was to compare a 3-hole versus a 4-hole sliding hip screw (SHS) in the presence of a retrograde intramedullary (RIM) nail for fixing intertrochanteric and comminuted midshaft femur fractures. METHODS: Mechanical tests were performed on 10 matched pairs of human cadaveric femurs that were osteotomized and then fixed using a 3-hole SHS versus the traditional "gold standard" 4-hole SHS in the presence of an RIM nail. RESULTS: Data showed no differences between the 3-hole SHS with RIM nail versus 4-hole SHS with RIM nail for stiffness (281 ± 127 vs. 260 ± 118 N/mm, P = 0.76), clinical failure at 10 mm of hip displacement (2014 ± 363 vs. 2134 ± 614 N, P = 0.52), or ultimate mechanical failure (3476 ± 776 vs. 3669 ± 755 N, P = 0.12). CONCLUSIONS: For this fracture pattern, a 3-hole SHS with RIM nail may be a suitable surgical alternative to the traditional "gold standard" method because it provides the same biomechanical properties while potentially reducing surgical time, blood loss, and hardware used.

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.047
GPT teacher head0.332
Teacher spread0.285 · 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 designBench or experimental
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

Citations10
Published2018
Admission routes1
Has abstractyes

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