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Record W2312173819 · doi:10.14740/jh235w

Evaluation of an Outpatient Model for Treatment of Acute Myeloid Leukemia

2016· article· en· W2312173819 on OpenAlexaffvenue
Andrew Aw, Mitchell Sabloff, Dawn Sheppard, David Allan, Harold Atkins, Isabelle Bence‐Bruckler, Carolyn Faught, Lothar Huebsch, Jason Tay, Kate Duke, Tim Ramsay, Christopher Bredeson

Bibliographic record

VenueJournal of Hematology · 2016
Typearticle
Languageen
FieldMedicine
TopicNeutropenia and Cancer Infections
Canadian institutionsOttawa Hospital
Fundersnot available
KeywordsMedicineMyeloid leukemiaLeukemiaMyeloidOncologyInternal medicine

Abstract

fetched live from OpenAlex

Background: In January 2012, our center developed an ambulatory model for acute myeloid leukemia (AML) patients receiving post-induction chemotherapy. The purpose of this study was to evaluate the feasibility and safety of the outpatient leukemia program at our center. Methods: A retrospective review of all consecutive AML patients receiving a first cycle of consolidation chemotherapy in the outpatient program in 2012 was compared to similarly managed patients primarily in the inpatient setting in 2010. Results: The 2012 cohort spent more days as outpatients in comparison to the 2010 cohort (median (range): 15.5 (0 - 27) vs. 0 (0 - 10), days, P = 0.002). There was no difference between the two cohorts in terms of median overall observation time (time from start of chemotherapy until discharged to clinic), transfusion requirements, days spent neutropenic or days spent febrile. There were no documented episodes of  Clostridium difficile , clinically significant bleeding, venous thromboembolism, or death in either cohort. Conclusions: Outpatient management of AML patients receiving post-induction chemotherapy was feasible in this group of carefully selected individuals, liberating limited and costly inpatient resources for more appropriate patients. J Hematol. 2016;5(1):1-7 doi: http://dx.doi.org/10.14740/jh235w

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.134

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.065
GPT teacher head0.372
Teacher spread0.307 · 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 designBench or experimental
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

Citations4
Published2016
Admission routes2
Has abstractyes

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