Healthcare resource utilization trends in patients with acute myeloid leukemia ineligible for intensive chemotherapy receiving first‐line systemic treatment or best supportive care: A multicenter international study
Bibliographic record
Abstract
OBJECTIVES: This retrospective chart review examined real-world healthcare resource utilization (HRU) in patients with AML ineligible for intensive therapy who received first-line systemic therapy or best supportive care (BSC). METHODS: Data were collected anonymously on patients with AML who initiated first-line hypomethylating agents (HMA), low-dose cytarabine (LDAC), other systemic therapy, or BSC. HRU endpoints included hospitalizations, outpatient consultations, transfusions, and supportive care. RESULTS: Of 1762 patients included, 46% received HMA, 11% received LDAC, 17% received other systemic therapy, 26% received BSC; median treatment durations were 118, 35, 33, and 57 days, respectively. Most patients were hospitalized, most commonly for treatment administration, transfusion, or infection (HMA 82%, LDAC 93%, other systemic therapy 83%, BSC 83%). A median number of hospitalizations were 2-6 across systemic groups and two for BSC, with median durations of 8-18 days. Transfusion rates and outpatient consultations were highest for HMA (80% and 79%) versus LDAC (57% and 53%), other systemic therapy (57% and 63%), and BSC (71% and 66%). Antivirals/antibiotics and antifungals were used more frequently than growth factors (72-92%, 34-63%, and 7-27%, respectively). CONCLUSION: Patients with AML ineligible for intensive therapy have high HRU; novel therapies are needed to alleviate this burden.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".