With great power comes great responsibility: Managing side effects of novel treatments in chronic lymphocytic leukemia (CLL)
Bibliographic record
Abstract
The treatment paradigm shift in CLL has uprooted many clinicians’ standard practices. Previously, treatment largely depended on age, organ function and “fitness” based on clinical trials which used CIRS (cumulative illness rating scale) scores. Today, as a hematologist who mainly treats patients with CLL, treatment strategies are more complex and multi-factorial. Treatments are based on molecular profiling, which aids in the identification of lower-risk patients for time-limited treatment options versus higher-risk patients (IGVH unmutated, del 17p or TP534 ) who benefit from continuous therapies. The highest-risk patients can be identified using a staging system for CLL known as the CLL-International Prognostic Index (CLL-IPI). However, increased CIRS scores are prognostic for poor outcomes independent of the CLL-IPI. As a result, selecting the right treatment for the right individual has never been more important, especially in the era of novel therapeutics. This treatment selection decision pathway includes understanding both patient factors and medical factors that may influence patient 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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".