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Record W3115728779 · doi:10.4103/ijpn.ijpn_133_20

Role of ultrasound in chronic pain management

2020· article· en· W3115728779 on OpenAlexaff
PhilipW H. Peng, RajendraKumar Sahoo

Bibliographic record

VenueIndian Journal of Pain · 2020
Typearticle
Languageen
FieldMedicine
TopicShoulder Injury and Treatment
Canadian institutionsToronto Western HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineUltrasoundChronic painPain managementPhysical therapyRadiology

Abstract

fetched live from OpenAlex

Ultrasound application in interventional pain medicine has made tremendous growth in recent years. Ultrasound not only avoids radiation exposure but also allows real-time visualization of the drug delivery and avoids damage to the surrounding structures. It also ensures accurate delivery of medication; thus improves efficacy and outcome. Furthermore, ultrasound is a valuable tool for pain physicians in confirming the diagnosis of many musculoskeletal pain conditions and entrapment neuropathies in the clinic. Its role in various aspects of chronic pain like spinal pain, musculoskeletal pain and nerve-related pain are discussed below.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.014
GPT teacher head0.274
Teacher spread0.260 · 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
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

Citations1
Published2020
Admission routes1
Has abstractyes

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