Quantifying Benefit of Autologous Transplantation for Relapsed Follicular Lymphoma Patients via Instrumental Variable Analysis
Bibliographic record
Abstract
The role of autologous stem cell transplantation (ASCT) in patients with relapsed follicular lymphoma (FL) remains controversial because of a lack of proven overall survival (OS) benefit versus nontransplant strategies. We conducted a comparative effectiveness research study involving 3 tertiary Canadian cancer centers to determine whether the ASCT-based approach used at 1 center improved OS relative to non-ASCT approaches used at the other centers. Of 1082 consecutive patients aged 18 to 60 years and diagnosed with FL from 2001 to 2010, the study population included 355 patients who experienced relapse from chemotherapy (center A = 96, center B = 84, center C = 175). Data were analyzed according to the instrumental variable of treatment center to control for confounding factors. The frequency of using ASCT at first or second relapse was significantly different between the centers (A = 58%, B = 7%, C = 5%, P < .001). With a median follow-up of 69.1 months, the actuarial 5-year OS rates after first chemotherapy relapse were 89%, 60%, and 60% for centers A, B, and C respectively (log rank P < .0001). Based on instrumental variable analysis, the use of ASCT at relapse 1 or 2 significantly decreased the risk of death from first relapse (HR .127, P = .004) and from initial diagnosis (HR .116, P = .004). In conclusion, for FL patients who relapse after chemotherapy, these results strongly support more frequent use of ASCT at first or second relapse.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".