Nanoparticle-Based Chemotherapy Formulations for Head and Neck Cancer: A Systematic Review and Perspectives
Bibliographic record
Abstract
Head and neck cancer (HNC) is a complex and heterogeneous disease associated with high mortality and morbidity worldwide. Standard therapeutic management of advanced HNC, which is based on radiotherapy often combined with chemotherapy, has been hampered by severe long-term side effects. To overcome these side effects, tumor-selective nanoparticles have been exploited as a potential drug delivery system to improve HNC therapy. A combination of MEDLINE, EMBASE, Cochrane Oral Health Group's Trials Register, Cochrane Central Register of Controlled Trials (CENTRAL) and ClinicalTrials.gov from inception up to June 2020 was used for this systematic review. A total of 1747 published manuscripts were reviewed and nine relevant references were retrieved for analysis, while eight of them were eligible for meta-analysis. Based on these studies, the level of evidence about the efficacy of nanoformulation for HNC therapy on tumor response and adverse side effects (SAE) was low. Even though basic research studies have revealed a greater promise of nanomaterial to improve the outcome of cancer therapy, none of them were translated into clinical benefits for HNC patients. This systematic review summarized and discussed the recent progress in the development of targeted nanoparticle approaches for HNC management, and open-up new avenues for future perspectives.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".