Updates from the 2016 American Society of Hematology Annual Meeting: Practice-Changing Studies in Untreated Follicular Lymphoma
Bibliographic record
Abstract
The 2016 annual meeting of the American Society of Hematology took place in San Diego, California, 3–6 December. At the meeting, results from key studies on the first-line treatment of follicular lymphoma were presented. Of those studies, key oral presentations included two analyzing data from the gallium study, which evaluated the efficacy and safety of obinutuzumab plus chemotherapy (G-chemo) compared with rituximab plus chemotherapy (R-chemo), followed, in responding patients with follicular lymphoma, by obinutuzumab or rituximab maintenance; results from the sabrina study, which evaluated the efficacy and safety of subcutaneous compared with intravenous rituximab; results of a cost-effectiveness analysis of first-line treatment with bendamustine and rituximab from a Canadian perspective; and results from the SAKK 35/10 study, which evaluated the safety and efficacy of rituximab plus lenalidomide compared with rituximab monotherapy. Our meeting report describes the foregoing studies and includes interviews with the Canadian investigators, plus commentaries by those investigators about the potential impact 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 | 0.059 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".