Case report: clozapine given in the context of chemotherapy for lung cancer
Bibliographic record
Abstract
BACKGROUND: Despite the frequent use of the antipsychotic medication, clozapine, in chronic treatments of psychiatric patients, there is limited clinical evidence available to guide clinicians in the problematic situation of a chemotherapy-induced blood dyscrasia. OBJECTIVE: To perform a literature review and add a case report to the available clinical evidence. METHOD: We gathered evidence through literature searches on Medline and with the assistance of a medical information specialist from Novartis who searched their internal database. We also report the case of a patient maintained on clozapine treatment despite full-dose chemotherapy (cisplatin and etoposide) for an extensive lung cancer. RESULT: The searches returned seven clinically relevant references. These references do not establish a synergistic effect of clozapine and chemotherapy on blood counts. However, it has been shown that clozapine exposure activates common apoptotic pathways shared with anticancer drugs. CONCLUSION: Although the meagre clinical evidence precludes drawing any general conclusion as to the safety of maintaining clozapine administration during chemotherapy, it does not point to an obvious worsening of the haematological outcomes.
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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.010 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".