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Record W2794192482 · doi:10.1177/1938640018758374

Plate Alone Versus Plate and Lag Screw for Lapidus Arthrodesis: A Biomechanical Comparison of Compression

2018· article· en· W2794192482 on OpenAlexaboutno aff
Peter K. Garas, Steven Thomas DiSegna, Abhay R. Patel

Bibliographic record

VenueFoot & Ankle Specialist · 2018
Typearticle
Languageen
FieldMedicine
TopicFoot and Ankle Surgery
Canadian institutionsnot available
Fundersnot available
KeywordsCadaveric spasmCompression (physics)Lag screwArthrodesisCompressive strengthSurgeryMaterials scienceOrthodonticsAnatomyMedicineComposite materialInternal fixation

Abstract

fetched live from OpenAlex

Background. Arthrodesis of the first tarsal metatarsal joint can be accomplished in many ways. The compressive force attained between various constructs remains unclear. This study compares compression achieved through a locking/compression Lapidus plate both with and without the addition of a lag screw. Methods: A dorsal medial Lapidus/locking compression plate (Total Compression Plate System, OrthoPro, Salt Lake City, UT, now Wright Medical) was applied to one cadaveric limb, while the same plate with the addition of a 4.0-mm cannulated lag screw was applied to the contralateral limb for a total of 5 matched pairs of cadaveric specimens. Compressive force was recorded over time and compared between the constructs using a compression sensor (8” FlexiForce Resistive Force Sensor, Phidgets Inc, Calgary, Alberta, Canada). Results: Compression was maintained for 45.4 minutes in the plate only construct, and 317 minutes with the addition of the lag screw ( P = .010). The mean time to 50% peak compression for the plate only construct was 4.90 minutes compared with 15.11 minutes for plate with lag screw construct (P = .012). Conclusion: The addition of a lag screw is recommended for extending the length of compression and possibly reducing nonweightbearing time and the risk of nonunion. Levels of Evidence: Level V

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.360
Threshold uncertainty score0.848

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.079
GPT teacher head0.351
Teacher spread0.272 · 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 designNot applicable
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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