Pain in chemotherapy-induced neuropathy – More than neuropathic?
Bibliographic record
Abstract
Chemotherapy-induced neuropathy (CIN) is an adverse effect of chemotherapy. Pain in CIN might comprise neuropathic and nonneuropathic (ie, musculoskeletal) pain components, which might be characterized by pain patterns, electrophysiology, and somatosensory profiling. Included were 146 patients (100 female, 46 male; aged 56 ± 0.8 years) with CIN arising from different chemotherapy regimens. Patients were characterized clinically through nerve conduction studies (NCS) and quantitative sensory testing (QST). Questionnaires for pain (McGill) and anxiety/depression (Hospital Anxiety and Depression Scale) were supplied. Patients were followed-up after 17 days. Large- (61%) and mixed- (35%) fibre neuropathies were more frequent than small-fibre neuropathy (1.4%). The 5 major chemotherapeutic regimens impacted differently on large- but not on small-fibre function and did not predict painfulness. Chronic pain associated with CIN was reported in 41.7%. Painless and painful CIN did not differ in QST profiles or electrophysiological findings, but different somatosensory patterns were found in CIN subgroups (pain at rest [RestP], n = 25; movement-associated pain [MovP], n = 15; both pain characteristics [MovP+RestP], n = 21; or no pain [NonP], n = 85): small-fibre function (cold-detection threshold, CDT: z score: -1.46 ± 0.21, P < 0.01) was most impaired in RestP; mechanical hyperalgesia was exclusively found in MovP (z score: +0.81 ± 0.30, P < 0.05). "Anxiety" discriminated between painful and painless CIN; "CDT" and "anxiety" discriminated between patients with ongoing (RestP) and movement-associated pain (MovP) or pain components (MovP+RestP). The detrimental effect of chemotherapy on large fibres failed to differentiate painful from painless CIN. Patients stratified for musculoskeletal or neuropathic pain, however, differed in psychological and somatosensory parameters. This stratification might allow for the application of a more specific therapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".