CLINICAL UTILITY OF INTERIM CT SCANS IN PATIENTS RECEIVING CHEMOIMMUNOTHERAPY FOR FIRST LINE TREATMENT OF FOLLICULAR LYMPHOMA
Bibliographic record
Abstract
rituximab with cyclophosphamide, doxorubicin, vincristine, and prednisone in 159 patients (52.6%).BMI was detected in 170 and 69 patients by BMB and PET/CT respectively.Sensitivity and accuracy were 46% and 73.2% for PET/CT and 84.3% and 92.4% for BMB respectively.In univariate analysis both PET/CT (p = 0.043; p = 0.01) and BMB (p = 0.004; p = 0.029) correlated with PFS and OS, respectively.In a multivariate analysis involving the whole series, only BMI-BMB (P = 0.043), but not BMI-PET-CT correlated with PFS.When considering those patients who received intensive treatment, only BMI-BMB (p = 0.004; p = 0.032) correlated with PFS and OS, respectively.Among histologic grade 3a patients, only BMI-BMB (p = 0.01; p = 0.009) correlated with PFS and OS, respectively.However, in other histologic grades (<3), BMI-BMB (P = 0.009) only correlated with PFS.BMI-BMB (P = 0.03) added independent prognostic value to POD-24 in the multivariate analysis.When PET/CT was used as positive BMI instead of BMB, it only added independent prognostic value in the intensive treatment cohort model (p = 0.026) and just for OS.Conclusions: In our FL series, BMI assessment by BMB was superior to that of PET/CT in both performance and prognosis and reinforce the need to carry out BMB for adequately fulfill FLIPI-2.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".