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Record W2806888518 · doi:10.1172/jci94003

A review of current theories and treatments for phantom limb pain

2018· review· en· W2806888518 on OpenAlexaff
Kassondra L. Collins, Hannah G. Russell, Patrick J. Schumacher, Katherine E. Robinson-Freeman, Ellen C. O’Conor, Kyla D. Gibney, Olivia Yambem, Robert W. Dykes, Robert S. Waters, Jack W. Tsao

Bibliographic record

VenueJournal of Clinical Investigation · 2018
Typereview
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsMcGill University
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentHealth Science Center, University of TennesseeU.S. Department of Veterans Affairs
KeywordsPhantom limbPhantom limb painAmputationPhantom painFeelingDorsal root ganglionPhysical medicine and rehabilitationNeuroscienceMedicinePeripheral nervous systemNervous systemQuality of life (healthcare)Central nervous systemDorsumPsychologySurgeryAnatomy

Abstract

fetched live from OpenAlex

Following amputation, most amputees still report feeling the missing limb and often describe these feelings as excruciatingly painful. Phantom limb sensations (PLS) are useful while controlling a prosthesis; however, phantom limb pain (PLP) is a debilitating condition that drastically hinders quality of life. Although such experiences have been reported since the early 16th century, the etiology remains unknown. Debate continues regarding the roles of the central and peripheral nervous systems. Currently, the most posited mechanistic theories rely on neuronal network reorganization; however, greater consideration should be given to the role of the dorsal root ganglion within the peripheral nervous system. This Review provides an overview of the proposed mechanistic theories as well as an overview of various treatments for PLP.

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: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.003

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 head0.319
GPT teacher head0.533
Teacher spread0.214 · 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

Citations181
Published2018
Admission routes1
Has abstractyes

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