Defining Spine Cancer Pain Syndromes: A Systematic Review and Proposed Terminology
Bibliographic record
Abstract
STUDY DESIGN: Systematic Review. OBJECTIVES: Formalized terminology for pain experienced by spine cancer patients is lacking. The common descriptors of spine cancer pain as mechanical or non-mechanical is not exhaustive. Misdiagnosed spinal pain may lead to ineffective treatment recommendations for cancer patients. METHODS: We conducted a systematic review of pain terminology that may be relevant to spinal oncology patients. We provide a comprehensive and unbiased summary of the existing evidence, not limited to the spine surgery literature, and subsequently consolidate these data into a practical, clinically relevant nomenclature for spine oncologists. RESULTS: Our literature search identified 3515 unique citations. Through title and abstract screening, 3407 citations were excluded, resulting in 54 full-text citations for review. Pain in cancer patients is typically described as nociceptive pain (somatic vs visceral), neurologic pain and treatment related pain. CONCLUSIONS: We consolidate the terminology used in the literature and consolidated into clinically relevant nomenclature of biologic tumor pain, mechanical pain, radicular pain, neuropathic pain, and treatment related pain. This review helps standardize terminology for cancer-related pain which may help clinicians identify pain generators.
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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 | 0.021 | 0.078 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".