Acute Leukemia in Patients Sixty Years of Age and Older
Bibliographic record
Abstract
OBJECTIVES: Acute leukemia, particularly acute myeloid leukemia, occurs more frequently in the elderly, a growing segment of the North American population. To evaluate our progress in the diagnosis, treatment and outcome of this condition, we reviewed our experience of all patients > or =60 years of age diagnosed with acute leukemia over a 20-year period at Saint Paul's Hospital, a university-based hospital in Vancouver, Canada. METHODS: A retrospective chart review was performed of 103 patients > or =60 years of age diagnosed with acute leukemia (acute myeloid leukemia-81; acute lymphoid leukemia-15; acute leukemia not otherwise specified-7). RESULTS: Median age was 72 (range 60-88) years. Bone marrow aspirate yielded cytogenetic information on 57 patients and 18 (31.6%) had an unfavourable karyotype. Fifty-three (51%) patients received induction chemotherapy (treated) and 50 (49%) were palliated (untreated). Treated patients were younger [median 67 years (range 60-79)] than untreated patients [76 years (61-88)], (P < 0.0001). Of the treated patients, 33 (62%) achieved a complete remission. The median overall survival for the group was 104 (1-2689) days, and for treated versus untreated patients-219 (1-2689) and 39 (2-1229) days, respectively (P = 0.0021). Univariate variables predictive of prolonged survival included induction chemotherapy (P = 0.0027), de novo leukemia (P = 0.0420), and younger age, with a relative increase in death in older subgroups (60-69, 70-79, 80+), (P = 0.0311). Induction chemotherapy was the only predictor of prolonged survival in multivariate analysis (P = 0.0027). CONCLUSIONS: The prognosis of acute leukemia in older patients remains poor, and even though induction chemotherapy seem to prolong survival in patients able to receive treatment, most ultimately die of leukemia.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".