Bibliographic record
Abstract
PURPOSE OF REVIEW: This review updates the clinical data on antiemetic therapy for chemotherapy classified as highly emetogenic. RECENT FINDINGS: A meta-analysis demonstrated that palonosetron was superior to other 5-hydroxytryptamine3 (5-HT3) receptor antagonists at least in the absence of aprepitant. Two major guideline groups have reclassified all chemotherapy that contains cyclophosphamide and an anthracycline as 'highly emetogenic'. Although recommended prophylaxis for drugs in that category includes aprepitant, phase II studies with cyclophosphamide, doxorubicin, vincristine and prednisone (CHOP) and doxorubicin, bleomycin, vincristine and dacarbazine (ABVD) demonstrated that single agent palonosetron alone provided control of emesis over 85% of patients. A randomized phase III trial of olanzapine versus aprepitant found that the control of emesis was similar and nausea was significantly better controlled with olanzapine. Two studies showed that there is no impact of the moderate cytochrome P450 3A4 (CYP3A4) inhibitor aprepitant on the pharmacokinetics of cyclophosphamide. Surveys in the United States and Europe demonstrated that antiemetic prescribing practices often do not adhere to guidelines even for highly emetogenic chemotherapy. SUMMARY: The major guideline groups recommend a combination of a 5-HT3 receptor antagonist, dexamethasone and aprepitant ('triple therapy') for treatment categorized as highly emetogenic. Recent data suggest that, although classified as highly emetogenic, palonosetron may provide very good control of emesis for CHOP and ABVD. Guidelines have not made firm recommendations for highly emetogenic chemotherapy administered over several days or stem cell transplant preparative regimens due to the lack of published randomized trials. Although well tolerated and effective, many patients receive suboptimal antiemetic therapy that includes aprepitant.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".