Updates from the 2017 American Society of Hematology Annual Meeting: Practice-Changing Studies in Untreated Chronic Lymphocytic Leukemia
Bibliographic record
Abstract
The 2017 annual meeting of the American Society of Hematology took place 9–12 December in Atlanta, Georgia. At the meeting, the oral presentations included results from key studies on the first-line treatment of chronic lymphocytic leukemia. A series of phase ii studies focusing on the efficacy and safety of novel treatment strategies were especially notable. One concerned the health-related quality of life results from the gibb study, which had examined the combination of obinutuzumab and bendamustine. A second evaluated the venetoclax–ibrutinib regimen in patients with high-risk disease. The third assessed the combination of ibrutinib, fludarabine, cyclophosphamide, and obinutuzumab in patients with mutated immunoglobulin heavy-chain variable region genes. The fourth examined the combination of ibrutinib, fludarabine, cyclophosphamide, and rituximab in younger patients. And the final study evaluated obinutuzumab–ibrutinib followed by a minimal residual disease strategy in fit patients. Our meeting report describes the foregoing studies and presents interviews with investigators and commentaries by Canadian hematologists about the potential effects on Canadian practice.
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 | 0.053 | 0.129 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".