Adverse event profiles and management of cisplatin-based chemoradiotherapy in locally advanced head and neck squamous cell carcinoma: a systematic review
Bibliographic record
Abstract
BACKGROUND: Patients with locally advanced squamous cell carcinoma of the head and neck (LA SCCHN) are treated with curative intent. Treatment guidelines recommend tumor resection, followed by adjuvant radiotherapy or chemoradiotherapy in patients with high risk of disease recurrence, or definitive chemoradiotherapy in patients who do not undergo surgery. When indicated, concurrent cisplatin provides benefit over radiotherapy alone but is highly toxic. In this systematic literature review, we review adverse event (AE) profiles of cisplatin-based chemoradiotherapy in patients with LA SCCHN and synthesize AE management strategies based on expert clinical opinion. METHODS: PubMed and Google Scholar were searched for relevant articles published between January 1, 2000, and January 9, 2024. Eligible articles characterized AE profiles associated with cisplatin-based chemoradiotherapy. Data on the percentage of patients with AEs were compared across cisplatin regimens. RESULTS: every 3 weeks (Q3W), incurring less acute grade 3-5 neutropenia, hepatotoxicity, nephrotoxicity and related electrolyte imbalances, and tinnitus in the postoperative setting, but cisplatin QW led to more frequent thrombocytopenia and pain. CONCLUSION: The relative efficacy of cisplatin Q3W versus QW remains under investigation. Cisplatin QW inherently increases the cumulative cisplatin dose more gradually, which may help in managing certain AEs. Treatment guidelines should be updated on the management of challenging AEs, including more detailed criteria on topics such as management of kidney failure including dialysis and timing of feeding tube placement.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".