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Record W3206706106 · doi:10.51731/cjht.2021.170

Bone Growth Stimulators for Treatment of Adults with Bone Disease or Injury

2021· article· en· W3206706106 on OpenAlexaboutno aff
Khai Tran, Jennifer Horton

Bibliographic record

VenueCanadian Journal of Health Technologies · 2021
Typearticle
Languageen
FieldMedicine
TopicBone fractures and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsLow-intensity pulsed ultrasoundMedicineContext (archaeology)RadiographyUltrasoundNonunionStimulationBone healingTherapeutic ultrasoundBone growthSurgeryRadiologyInternal medicine

Abstract

fetched live from OpenAlex

Electrical stimulation and low intensity pulsed ultrasound technologies for bone healing may have some beneficial effects on radiographic assessment outcomes (e.g., spinal fusion rate, radiographic nonunion rates, number of days to radiographic healing) and clinical assessment outcomes (e.g., mouth opening, wrist and shoulder mobility, exteroceptive sensation, and wound healing), but may not improve outcomes that are important to patients (e.g., functional recovery). Pain may be reduced by electrical bone growth stimulation devices, but not by low intensity pulsed ultrasound. No adverse events (AEs) related to the low intensity pulsed ultrasound device were reported. It is unknown if there are AEs related to electrical stimulation devices (no evidence found). Low intensity pulsed ultrasound for treatment of fresh tibial fractures was not cost-effective compared with placebo from either a payer perspective or societal perspective within the Canadian context. It is unknown if electrical bone growth stimulator devices are cost-effective (no evidence found).

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.037
Threshold uncertainty score0.091

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0270.002

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 head0.021
GPT teacher head0.297
Teacher spread0.276 · 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 designNot applicable
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
Published2021
Admission routes1
Has abstractyes

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