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Record W3038932691 · doi:10.1142/s021895772030001x

COMPRESSION SCREWS IN THE TREATMENT OF FRACTURES: A SCOPING REVIEW

2020· review· en· W3038932691 on OpenAlexaff
Christopher Vannabouathong, Justin Chiu, Pei Ye Li, Shakib Akhter, Nasir Hussain, Naushin S. Sholapur, Mohit Bhandari

Bibliographic record

VenueJournal of Musculoskeletal Research · 2020
Typereview
Languageen
FieldMedicine
TopicHip and Femur Fractures
Canadian institutionsMcMaster University
FundersAcumed
KeywordsMedicineAvulsion fractureAvulsionInternal fixationRandomized controlled trialEvidence-based medicineSurgeryOrthodonticsPhysical therapy

Abstract

fetched live from OpenAlex

The purpose of this scoping review was to identify and describe the current body of evidence on internal fracture fixation devices that require compression screws. Electronic literature searches were conducted and 665 unique studies were included. Investigations on avulsion fractures represented a small proportion of these studies (45 studies), the remainder being those on patients with fractures with vascular compromise risk. The most common type of avulsion injury investigated was tibial avulsion fractures, followed by fifth metatarsal and calcaneal avulsions. For vascular compromised fractures, greater than half of the studies were on femoral neck injuries while scaphoid injury studies also represented a substantial proportion of this evidence base. Most of the studies were case series and there was limited randomized clinical trial evidence. A total of 429 studies provided sufficient information on the devices used in their investigation; DePuy-Synthes and Acumed ranked as the top two companies in cumulative publications over the last decade. There is a large body of evidence on internal fixation compression screw devices used in the management of fractures; however, the literature is generally dominated by non-comparative research studies. Opportunities to improve the evidence base are large. Manufacturer priority on evidence-based product development is key to ongoing research inquiry.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.823
Threshold uncertainty score0.916

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.226
GPT teacher head0.576
Teacher spread0.350 · 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 designOther design
Domainnot available
GenreReview

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
Published2020
Admission routes1
Has abstractyes

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