Bibliographic record
Abstract
Approximately one half of cancer patients will experience nausea or vomiting during the course of their disease either because of the cancer itself or because of their treatment. Emesis attributable to cancer warrants a careful investigation to determine whether a treatable underlying cause is responsible. Interventions using dexamethasone and octreotide may reduce vomiting attributable to bowel obstruction. In the absence of a bowel obstruction or a correctable cause, the usual approach is a sequential trial of antiemetics guided by considerations of cost and side effects.Major progress in managing chemotherapy-induced emesis followed from the use of a combination of a corticosteroid and 5-hydroxytryptamine(3) (5-HT(3)) receptor antagonist for moderately to highly emetogenic chemotherapy. Nevertheless, vomiting still occurred in approximately 40% of women receiving chemotherapy containing an anthracycline plus cyclophosphamide and in approximately 50% of patients receiving high-dose cisplatin. The addition of aprepitant, a neurokinin 1 receptor antagonist, improved control of emesis by a further 15%-20%, and that agent is now recommended as part of standard antiemetic therapy for patients at high risk of emesis. Based largely on anecdotal experience, cannabinoids and olanzapine are sometimes also recommended in patients with refractory emesis. Phase III trials are required to confirm their efficacy as add-ons to a corticosteroid, a 5-HT(3) receptor antagonist, and possibly 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".