Additional file 1 of Clinical outcomes in patients relapsed/refractory after ≥2 prior lines of therapy for follicular lymphoma: a systematic literature review and meta-analysis
Bibliographic record
Abstract
Additional file 1: Table S1. Embase search strategy. Table S2. Cochrane Central Register of Controlled Trials search strategy. Table S3. MEDLINE search strategy. Table S4. Study selection criteria to identify trials for the systematic literature review. Table S5. Studies included in each meta-analysis. Table S6. Study quality assessment results. Table S7. Response criteria used for each study included in the meta-analysis. Table S8. Meta-analysis of response outcomes, separated by treatment category, a. Main analysis, b. With inclusion of off-label treatments. Table S9. Model selection across the Bayesian analyses. Table S10. Time-to-event meta-analysis results for supplemental models. Fig. S1. Meta-analysis of response outcomes, including off-label treatments, A) Overall response rate, B) Complete response. Fig. S2. Pooled KM curves for supplemental model including off-label treatments, A) PFS 3rd line plus, B) OS 3rd line plus, Fig. S3. Pooled KM curves for supplemental model including only SCT studies, A) PFS 3rd line plus, B) OS 3rd line plus.
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 | 0.004 | 0.042 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.747 | 0.030 |
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".