Residual Limb Pain Is Not a Diagnosis
Bibliographic record
Abstract
BACKGROUND: Although postamputation pain (PAP) syndromes have been described since the 16th century, taxonomy of these conditions remains ill-defined. The term "Residual Limb Pain" fails to distinguish between distinct diagnostic entities such as neuroma, complex regional pain syndrome, and somatic pathology. Even phantom limb pain (PLP), although easily distinguished from residual limb pain (RLP), has not been consistently delineated from other PAP syndromes. METHODS: A systematic review of the literature was conducted to identify the degree of delineation of various post amputation pain states and what diagnostic criteria were utilized if any. Furthermore, papers that involved treatment modalities were reviewed to determine efficacy of treatment. RESULTS: Of the 151 papers reviewed, none further categorized RLP into more specific diagnostic criteria. Furthermore, the literature contains numerous case reports, case series, letters to the editors, and grossly underpowered studies demonstrating significant positive results, yet few high-quality randomized controlled trials. CONCLUSIONS: Describing and defining the distinct clinical entities, intuitively, is a prerequisite to developing optimal treatments. The reported variation in the incidence of PAP phenomena may well represent inconsistency in assessment tools and diagnostic categories rather than variation in prevalence of these conditions. In this paper, we review the historical evolution of the current understanding of these syndromes and propose an algorithm for uniform classification.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".