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Percent of Peripheral Blood Leukemic Blasts (PPBLB) at Diagnosis as a Predictor of Short- and Long-Term Survival in Acute Myeloid Leukemia (AML).

2006· article· en· W2582847085 on OpenAlexaffabout
Eden Story, Marc Prud’homme-Foster, Mitchell Sabloff

Bibliographic record

VenueBlood · 2006
Typearticle
Languageen
FieldMedicine
TopicAcute Myeloid Leukemia Research
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineUnivariate analysisAcute promyelocytic leukemiaInternal medicineMyeloid leukemiaLeukemiaBone marrowSurgeryPopulationMultivariate analysisGastroenterology

Abstract

fetched live from OpenAlex

Abstract Introduction: The diagnosis of AML and the determination of remission status depend on the percent of leukemic blasts in the bone marrow. The PPBLB is not routinely used in the diagnosis or prognosis of AML. Recently, in an analysis of patients with AML with a greater PPBLB compared to bone marrow (1/4 of the patients with AML that were studied), it was noted that a high PPBLB had a negative influence on overall survival (OS) in a univariate analysis (Amin HM. Leukemia 2005 19:1567), but this did not carry through in the multivariate analysis. We sought to examine the clinical impact of an elevated PPBLB on outcome, either early or late, in our local population of patients with AML. Methods: All patients, previously untreated with a new diagnosis of AML admitted to the Ottawa Hospital between 2000 and 2004 were examined in this study. Patients were excluded if they had a bone marrow transplant, acute promyelocytic leukemia or secondary AML. Results: 70 patients with a median age of 67.8 years were reviewed. After removing all the excluded patients only 33 received treatment (13 received 1–2 cycles of induction alone and 20 received consolidation). The median age at diagnosis was 62 years. Complete remission (CR) was achieved in 22/33 (67%) of the patients. 2 of these patients did not proceed to consolidation. The median follow-up from diagnosis in those who achieved a CR was 635 days. 6 patients died from treatment-related complications, all during induction. Upon examining OS based on the PPBLB at diagnosis it appeared that the largest differences were achieved at a cut-off of 10% PPBLB. Therefore, this cut-off was used to define high (>10%) vs. low PPBLB (<10%). Amongst the 33 patients who received treatment, 26 patients had a high PBBLB and 7 had a low PPBLB. The OS based on the PPBLB is shown in Figure 1. A trend was noted towards a lower OS with high PPBLB (p=0.1). In patients who achieved a CR (n = 22/33), 17 had a high PPBLB and 5 had a low PPBLB at diagnosis. The PPBLB was not found to be statistically significant with respect to achieving CR or early mortality but the trend favoring a low PPBLB in OS was preserved in patients who had achieved a CR (p=0.08). The PPBLB was not found to be associated with functional abnormalities of the bone marrow as determined by a lack of association with neutropenia and anemia, nor was it correlated with cytogenetics. Conclusion: PPBLB may be predictive of long-term OS. Early mortality or the ability to achieve CR did not seem to be influenced by the PPBLB. Therefore, a high PPBLB may reflect a surrogate marker of an aggressive phenotype. This might also further support the idea that the process by which these leukemic blasts exit the bone marrow is not random or due to a “pushing” phenomenon, but rather managed by the presence or lack of adhesion molecules, coordinating this movement. Therefore, the PPBLB may be useful alone or as part of a predictive model of long-term OS of AML at diagnosis. We hope to confirm these findings in a larger patient population, exploring the impact of PPBLB on clinical outcomes. Figure 1. OS of ALL Treated Patients Figure 1. OS of ALL Treated Patients

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.013
GPT teacher head0.268
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2006
Admission routes2
Has abstractyes

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