Assessment tools for chemotherapy-induced peripheral neuropathy: a narrative review of clinician, patient-reported, and objective measures
Bibliographic record
Abstract
BACKGROUND: Chemotherapy-induced peripheral neuropathy (CIPN) is a common side effect of chemotherapy, affecting motor, sensory, and autonomic function. Accurate assessment is important during treatment, when CIPN may necessitate dose reductions or discontinuation, and after treatment, as chronic CIPN can greatly impact quality of life and safety in cancer survivorship. Measurement tools can include subjective measures, including clinician-based grading scales or patient-reported outcome measures (PROMs), and objective measures. This review aimed to summarize current CIPN assessment tools, highlighting characteristics such as feasibility, minimum clinically important differences (MCIDs), validity and reliability to allow for comparison and selection of tools by clinicians and researchers. METHODS: Following the Scale for the Assessment of Narrative Review Articles methodology guidelines, 2 investigators performed a comprehensive literature search using predefined search terms relating to CIPN measurement. Additional papers were identified through a search of prior systematic reviews and tracing back references from key articles. Data were extracted from source papers and any available appendices. RESULTS: We identified 3 clinician-based grading scales, 20 PROMs, and 8 objective measurement tools. While the majority of tools have been validated for neuropathy, a minority of them have established MCIDs and validation in CIPN-specific populations. CONCLUSIONS: Tool selection should align with the specific needs of clinicians and researchers. Instruments that are valid, reliable, and assess multiple CIPN domains are recommended. Further research is needed to validate many of these tools in CIPN-specific populations and to determine their MCIDs.
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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 | 0.022 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".