Making sense of phantom limb pain
Bibliographic record
Abstract
Phantom limb pain (PLP) impacts the majority of individuals who undergo limb amputation. The PLP experience is highly heterogenous in its quality, intensity, frequency and severity. This heterogeneity, combined with the low prevalence of amputation in the general population, has made it difficult to accumulate reliable data on PLP. Consequently, we lack consensus on PLP mechanisms, as well as effective treatment options. However, the wealth of new PLP research, over the past decade, provides a unique opportunity to re-evaluate some of the core assumptions underlying what we know about PLP and the rationale behind PLP treatments. The goal of this review is to help generate consensus in the field on how best to research PLP, from phenomenology to treatment. We highlight conceptual and methodological challenges in studying PLP, which have hindered progress on the topic and spawned disagreement in the field, and offer potential solutions to overcome these challenges. Our hope is that a constructive evaluation of the foundational knowledge underlying PLP research practices will enable more informed decisions when testing the efficacy of existing interventions and will guide the development of the next generation of PLP treatments.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".