Autologous haematopoietic cell transplantation for non‐<scp>H</scp>odgkin lymphoma with secondary <scp>CNS</scp> involvement
Bibliographic record
Abstract
Pre-existing central nervous system (CNS) involvement may influence referral for autologous haematopoietic cell transplantation (AHCT) for patients with non-Hodgkin lymphoma (NHL). The outcomes of 151 adult patients with NHL with prior secondary CNS involvement (CNS(+) ) receiving an AHCT were compared to 4688 patients without prior CNS lymphoma (CNS(-) ). There were significant baseline differences between the cohorts. CNS(+) patients were more likely to be younger, have lower performance scores, higher age-adjusted international prognostic index scores, more advanced disease stage at diagnosis, more aggressive histology, more sites of extranodal disease, and a shorter interval between diagnosis and AHCT. However, no statistically significant differences were identified between the two groups by analysis of progression-free survival (PFS) and overall survival (OS) at 5 years. A matched pair comparison of the CNS(+) group with a subset of CNS(-) patients matched on propensity score also showed no differences in outcomes. Patients with active CNS lymphoma at the time of AHCT (n = 55) had a higher relapse rate and diminished PFS and OS compared with patients whose CNS lymphoma was in remission (n = 96) at the time of AHCT. CNS(+) patients can achieve excellent long-term outcomes with AHCT. Active CNS lymphoma at transplant confers a worse prognosis.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".