Characterizing the ideal patient for treatment with inotuzumab ozogamicin for relapsed/refractory acute lymphoblastic leukemia: a systematic literature review
Bibliographic record
Abstract
INTRODUCTION: Inotuzumab ozogamicin (InO) is indicated for the treatment of adults with relapsed or refractory (R/R) acute lymphoblastic leukemia (ALL). This systematic literature review (CRD42022330496) assessed outcomes by baseline characteristics for patients with R/R ALL treated with InO to identify which patients may benefit most. METHODS: In adherence with PRISMA guidelines, searches were run in Embase and MEDLINE. Inclusion criteria were real-world evidence, observational studies, and phase 2-4 trials. The Cochrane Risk of Bias tool and Newcastle-Ottawa instrument assessed quality. RESULTS: 34 publications were included; 11 described the phase 3 INO-VATE trial. Patients treated with InO who were CD22-positive, in first salvage, and eligible for subsequent hematopoietic stem cell transplant (HSCT) had improved outcomes. Reduced incidence of veno-occlusive disease was observed in patients with normal transaminase levels and bilirubin, no prior liver disease, and who did not receive dual alkylators. CONCLUSIONS: The ideal patient for InO treatment has CD22-positive disease (≥20% leukemic blasts), normal liver function, no history of liver disease, is in first salvage, has not previously received HSCT, prefers outpatient treatment, or has high disease burden. Limitations included potentially missing publications that were non-English, not identified in the searches, or available after the date the searches were conducted. REGISTRATION: This systematic review was registered on the Prospective Register of Systematic Reviews (PROSPERO), registration number: CRD42022330496.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".