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Record W2064153027 · doi:10.1002/ajh.22268

Feasibility of outpatient consolidation chemotherapy in older versus younger patients with acute myeloid leukemia

2011· article· en· W2064153027 on OpenAlexaff
Lalit Saini, Mark D. Minden, Andre C. Schuh, Karen Yee, Aaron D. Schimmer, Vikas Gupta, Eshetu G. Atenafu, Cindy Murray, Shannon Nixon, Joseph Brandwein

Bibliographic record

VenueAmerican Journal of Hematology · 2011
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsPrincess Margaret Cancer CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineMyeloid leukemiaNeutropeniaCohortChemotherapyBacteremiaInternal medicineLeukemiaMortality rateFebrile neutropeniaPediatricsIntensive care medicineAntibiotics

Abstract

fetched live from OpenAlex

Intensive consolidation chemotherapy for acute myeloid leukemia (AML) patients in complete remission is being increasingly administered on an outpatient basis. Although this approach has been found to be safe and feasible in younger patients, its safety in older patients remains unknown. We therefore undertook an evaluation of outpatient-based consolidation chemotherapy in older AML patients, and compared results to younger patients treated at the same institution over the same time period. The overall rate of readmission was ~50%, mostly for infections, with mean admission duration of 2 weeks. The overall mortality rate was 2.2%. Readmission rates and duration of readmission were somewhat higher in older patients, but infection rate, intensive care (ICU) admissions, and mortality rates were comparable to those in the younger patient cohort. However, we also observed that rates of febrile neutropenia, bacteremia, ICU admission, and death were significantly higher during the second consolidation, as compared with the first, in both younger and older patients. We conclude that outpatient-based consolidation therapy can be safely undertaken in a substantial proportion of fit older patients with AML.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.011
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.290
Teacher spread0.269 · 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 teacher head, 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".

Quick stats

Citations20
Published2011
Admission routes1
Has abstractyes

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