Peripheral Neuropathy Instruments for Individuals with Cancer: A COSMIN-Based Systematic Review of Measurement Properties
Bibliographic record
Abstract
Although the literature on patient-reported outcomes (PROMs) continues to expand, challenges persist in selecting reliable and valid instruments for assessing peripheral neuropathy (PN) in patients with cancer. This systematic review aimed to identify all validated self-report PN scales and critically appraise their measurement properties. This review was conducted using the COSMIN methodology for PROMs and the PRISMA statement. Five databases were searched from inception to August 2024, identifying 46 eligible studies and 16 PROMs. Evidence quality ranged from "very low" to "moderate", with notable inconsistencies in the content and structural validity phases of most instruments. Instruments such as the Chemotherapy-induced peripheral neuropathy assessment tool and the Functional Assessment of Cancer Therapy/Gynecologic Oncology Group-Neurotoxicity demonstrated moderate quality and potential utility in clinical practice, while others, including the Location-based assessment of sensory symptoms in cancer and the Measure of Ovarian Symptoms and Treatment, had insufficient evidence to support their use. Importantly, all PROMs focused on chemotherapy-induced peripheral neuropathy, highlighting a significant gap in instruments addressing other PN causes, such as radiotherapy or tumor-related nerve damage. Further research should prioritize developing and validating instruments for distinct cancer populations, ensuring robust psychometric properties and clinical applicability.
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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 | 0.024 | 0.090 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| 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".