Personalized Risk Assessment for Taxane-Induced Hypersensitivity Reactions: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: Taxanes, including paclitaxel and docetaxel, are widely used in cancer treatment but frequently cause hypersensitivity reactions (HSRs), disrupting treatment continuity. This meta-analysis aimed to identify consistent risk factors for taxane-induced HSRs to support personalized risk assessments and optimize therapeutic outcomes. Methods: This systematic review and meta-analysis followed the PRISMA guidelines and was registered with PROSPERO (CRD42023476738). Comprehensive literature searches were conducted up to 30 June 2024. The quality of the studies was assessed using the Newcastle-Ottawa Scale. Data were synthesized to calculate pooled odds ratios (ORs) and 95% confidence intervals (CIs), using fixed or random effects models. Results: A total of 18 studies of moderate or higher quality were included, involving 8333 patients. The incidence of HSRs ranged from 3.0% to 33.1%. Risk factors assessed included history of allergy, obesity, postmenopausal state, ovarian cancer, and H2 receptor antagonist (H2RA) premedication. A history of allergy was identified as a potential risk factor with marginal significance (OR 1.85, 95% CI 0.97–3.54, p = 0.06), while H2RA premedication, ovarian cancer, and female sex were not significantly associated with HSR risk. Substantial heterogeneity was observed for obesity (I2 = 57.71%, p = 0.069) and postmenopausal status (I2 = 78.98%). Conclusions: This study highlights the complex nature of taxane-induced HSRs and emphasizes the need for personalized risk assessments. While a history of allergy is a potential risk factor, heterogeneity across other factors underscores the importance of individualized approaches. Clinicians should tailor strategies to balance the benefits of taxane therapy with patient-specific risks to improve clinical 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 | 0.023 | 0.043 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.056 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".