Barriers to enrollment of patients with recurrent diffuse large B-cell lymphoma (DLBCL) in clinical trials.
Bibliographic record
Abstract
e19520 Background: Research into novel therapies for relapsed/refractory DLBCL may be hindered by the perception these patients are difficult to capture in clinical trials. We performed a retrospective analysis of all DLBCL at our institution from 01/2006 to 03/2012 to identify hurdles to trial enrolment. Methods: All DLBCL cases were identified through the hospital tumor registry. Patients were included in the analysis if they had any diagnosis of DLBCL relapsed or refractory to standard therapy. Baseline demographic and clinical characteristics, details of treatment, responses, relapse, evaluation for clinical trials and participation in clinical trials were determined by review of hospital charts. Results: Of a total of 284 patients, 76 had relapsed/refractory disease, 10 of 20 had a successful ASCT, and there is insufficient data on 1 patient. Of the remaining 65, 11 (17%) made it to trial. The median age was 65, 34 were male, median number of prior therapies was 2, 74% had at least one comorbidity and 46% had at least 2, 62% of patients had de novo DLBCL, 18% transformed and 20% composite. 81% of cases were discussed at tumor board. Reasons for failing to enroll on trial included prohibitive comorbidity (21%), rapid progression (15%), decision for palliation (15%), prior second malignancy (9%), thrombocytopenia (13%), CNS disease (9%), proximity to ASCT (2%), no protocol for DLBCL (6%), palliative radiation (6%). Conclusions: We demonstrate that 17% of patients with DLBCL not responding to standard therapy make it to trial, the remainder does not mainly beacause of comorbidity and rapid progression. Similar barriers were found for solid tumor patients (Lara, JCO 2001) and a similar accrual rate was seen in relapsed non-small cell lung cancer (Baggstrom,J Thor Oncol 2011) . Relapsed DLBCL is a population for whom clinical trial research is challenging but possible.
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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 | 0.212 | 0.271 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".