Bibliographic record
Abstract
Abstract The field of metal‐based compounds intended for treatment of cancer has long been dominated by cisplatin, first approved for clinical use in treating testicular cancer, in 1978. By all accounts, cisplatin has been a resounding success. Other platinum‐based compounds have been introduced since then, with carboplatin and oxaliplatin serving to broaden the range of tumors that can be effectively treated. Overcoming tumor cell resistance is an ongoing problem, and the target of many newer platinum‐based anticancer therapeutic agents. More recently, substitution of platinum with other metal ions, such as ruthenium, gallium, lanthanides, and arsenic, has expanded the array of potential metal‐based anticancer agents, with the arsenic trioxide having particular success against acute promyelocytic leukemia (APL). A key feature of metal‐based anticancer therapeutic agents is that they are toxic by design: without toxicity, the tumor cells could not be eliminated. Similar strategies are useful for diseases such as malaria, Chagas disease, and leishmaniasis, in which the target organisms are parasites. For parasitic diseases, as well as for resistant bacterial infections, or to stop tumor growth, the requirement for appropriate metallotherapeutic design is to minimize contact with healthy tissue, to target particular cells, and to avoid premature release of the metal ion.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".