Exploring Analgesic Use Patterns Among Cancer Survivors With Chronic Chemotherapy-Induced Peripheral Neuropathy
Bibliographic record
Abstract
OBJECTIVES: To explore cancer survivors' historical and current use of analgesics for chronic chemotherapy-induced peripheral neuropathy (CIPN). SAMPLE & SETTING: 142 post-treatment cancer survivors who received neurotoxic chemotherapy and were experiencing moderate to severe CIPN. METHODS & VARIABLES: Participants completed the Treatment-Induced Neuropathy Assessment Scale at baseline and reported all analgesics used to manage CIPN. Frequency of historical or current prescription analgesic use for chronic CIPN was described and stratified by CIPN pain severity. RESULTS: At baseline, 31% of participants reported historical use of analgesics for CIPN and 46% of participants were currently using analgesics for CIPN. Gabapentin was the most frequently used analgesic, historically (20%) and currently (34%), and duloxetine was used less frequently (6% historical use, 10% current use). Many participants with severe pain (59%) reported using analgesics for CIPN. IMPLICATIONS FOR NURSING: Duloxetine, the first-line treatment for chronic CIPN pain, was used less frequently than gabapentin, a common prescription analgesic for neuropathic pain. Further research is needed to determine strategies to promote the implementation of evidence-based CIPN treatments in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".