Assessing the prognostic scoring models to predict patient outcomes in AIDS‐related diffuse large B‐cell lymphoma
Bibliographic record
Abstract
To the EditorLymphomas remain a leading cause of cancer morbidity and mortality for HIV infected patients, and have increased incidence even in the era of antiretroviral therapy (ART), and AIDS-related diffuse large B cell lymphoma (AR-DLBCL) is one of the most common AIDS-related lymphoma subtypes.1 To date, data regarding prognostic markers of patients with AR-DLBCL is scarce, and no specific prognostic model exists for these patients.Simple and accessible prognostic models must be developed for patients with AR-DLBCL risk stratification.A recent study investigated the prognostic risk factors for overall survival (OS) and progression-free survival (PFS) in AR-DLBCL, and first constructed prognostic models for risk stratification of AR-DLBCL patients.2 The OS prognostic model comprises indicators such as central nervous system (CNS) involvement, lymphoma diagnosis with opportunistic infection (OI), and elevated lactate dehydrogenase (LDH).The indicators that make up the PFS prognostic model include CNS involvement, diagnosis of lymphoma with OI, elevated LDH, and more than four cycles of chemotherapy.These prognostic indicators were clinically simple and readily available, and the prognostic scoring models could stratify patients with AR-DLBCL for prognostic determinations and might have implications for clinical decisionmaking.A subgroup analysis of patients with diverse clinical and molecular characteristics, including age, tumor stage, and molecular subtypes, was performed to mandate the stratification of the patient cohort and repetition of the statistical analyses for each subgroup.
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 | 0.011 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".