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Record W4318389358 · doi:10.29173/writingacrossuofa45

What is the Most Effective, Non-Pharmacological, and Non-Invasive Treatment Mechanism for Reducing Phantom Limb Pain in Amputees?

2023· article· en· W4318389358 on OpenAlexvenueno aff
Sabrina Rivelazione

Bibliographic record

VenueWriting across the University of Alberta · 2023
Typearticle
Languageen
FieldMedicine
TopicPain Management and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsPhantom limb painPhantom limbMechanism (biology)MedicineQuality of life (healthcare)Phantom painPhysical medicine and rehabilitationPhysical therapyAmputationSurgeryNursing

Abstract

fetched live from OpenAlex

Phantom limb pain (PLP) is characterized by pain deposited in non-existent extremities. Although present in those born without limbs, nearly 90% of amputees experience PLP. Unfortunately, the causal mechanism of PLP is unknown, making it difficult to treat. Researchers are striving to find the most efficacious treatment as 25–50% of patients report severe pain, which reduces their quality of life. Currently, there is an immense list of treatments all with various degrees of success. This article explores some of these potential treatments.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.015
GPT teacher head0.278
Teacher spread0.263 · 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 designQualitative
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

Citations0
Published2023
Admission routes1
Has abstractyes

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