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Efficacy of Combined Magnetic Field Treatment on Spinal Fusion: A Review of the Literature

2016· review· en· W2555530396 on OpenAlexaff
Mark Phillips, Brian Drew, Sheila Sprague, Ilyas Aleem

Bibliographic record

VenueJournal of Long-Term Effects of Medical Implants · 2016
Typereview
Languageen
FieldMedicine
TopicBone health and osteoporosis research
Canadian institutionsMcMaster University Medical CentreMcMaster University
Fundersnot available
KeywordsMedicineNonunionSpinal fusionAdjunctComplicationSurgeryPhysical therapy

Abstract

fetched live from OpenAlex

Vertebral fractures place significant burdens on patients, caregivers, and the healthcare system at large. Nonunion is a frequent complication following vertebral fracture treatment, often resulting in significant patient pain and morbidity. Bone growth stimulators are an adjunct treatment modality proposed to increase rates of fracture healing. Combined magnetic field (CMF) bone growth stimulators have specifically been shown to increase union rates in patients with vertebral fractures. Certain populations, such as the elderly, postmenopausal women, and smokers, have demonstrated a preferentially greater response to CMF treatment. The present review focuses on the mechanism of action, efficacy, cost effectiveness, indications, contraindications, and safety of CMF treatment as an adjunct treatment for vertebral fractures. Future large high-quality investigations of adjunct CMF treatment for spine fusion patients focusing on patient-important outcomes are warranted.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.034
GPT teacher head0.419
Teacher spread0.385 · 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 designSystematic review
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

Citations2
Published2016
Admission routes1
Has abstractyes

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