Non-pharmacological interventions for side effects of antineoplastic chemotherapy prioritized by patients: systematic review
Bibliographic record
Abstract
Introduction: Different non-pharmacological interventions have been studied to manage symptoms derived from chemotherapy, but their effectiveness is unknown. Objective: To describe non-pharmacological interventions for managing symptoms secondary to antineoplastic chemotherapy in adults. Materials and Methods: Systematic review of analytical experimental and observational studies (2021 to 2023). The studies were selected, and data was extracted in parallel. Discrepancies were resolved with a third reviewer. The risk of bias was assessed using the Risk of Bias (RoB) tool and The Newcastle-Ottawa Scale (NOS). The literature was synthesized descriptively based on prioritized outcomes. Results: The prioritized outcomes were neutropenia, pain, neuropathy, nausea, vomiting, alopecia, anorexia, and sleep disorders. Out of 7520 references found, 62 were included for analysis. Acupressure showed a possible effect in controlling symptoms such as nausea and vomiting. The intervention with cold on the scalp showed differences in the stages of alopecia severity. Other interventions showed heterogeneity. Discussion: Non-pharmacological interventions have been widely described in observational and experimental studies in the control of side effects of chemotherapy; however, there is homogeneity and a high risk of bias. Conclusion: Acupressure, muscle massage, music therapy, foot baths, and other interventions have been studied for nausea, vomiting, sleep disorders, neutropenia, alopecia, anorexia, pain, and neuropathy as secondary symptoms prioritized by patients. It is necessary to standardize both the interventions and how measure the outcomes.
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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 | 0.018 | 0.084 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".