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Record W2487244003 · doi:10.1016/j.bbmt.2016.07.013

Personalizing Busulfan-Based Conditioning: Considerations from the American Society for Blood and Marrow Transplantation Practice Guidelines Committee

2016· review· en· W2487244003 on OpenAlexaff
Jeanne Palmer, Jeannine S. McCune, Miguel‐Angel Perales, David I. Marks, Joseph Bubalo, Mohamad Mohty, John R. Wingard, Angélo Paci, Moustapha Hassan, Christopher Bredeson, Joseph Pidala, Nina Shah, Paul Shaughnessy, Navneet S. Majhail, Jeff Schriber, Bipin N. Savani, Paul A. Carpenter

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2016
Typereview
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsOttawa Hospital
FundersNational Cancer InstituteU.S. Food and Drug Administration
KeywordsMedicineBusulfanBone marrow transplantationTransplantationConditioning regimenIntensive care medicineInternal medicineHematopoietic stem cell transplantation

Abstract

fetched live from OpenAlex

The Practice Guidelines Committee of the American Society of Blood or Marrow Transplantation (ASBMT) sought to develop an evidence-based review about personalizing busulfan-based conditioning. The Committee sought to grade the relevant published studies (June 1, 2008 through March 31, 2016) according to criteria set forth by the Steering Committee for Evidence Based Reviews from ASBMT. Unfortunately, the published literature was too heterogeneous and lacked adequately powered and sufficiently controlled studies for this to be feasible. Despite this observation, the continued interest in this topic led the Practice Guidelines Committee to develop a list of most frequently asked questions (FAQs) regarding personalized busulfan dosing. This "Considerations" document is a list of these FAQs and their responses, addressing topics of practical relevance to hematopoietic cell transplantation clinicians.

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)0.007
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0030.001

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 head0.069
GPT teacher head0.365
Teacher spread0.295 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations165
Published2016
Admission routes1
Has abstractno

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