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Record W2065719321 · doi:10.1080/1024533021000008209

Treatment of Lymphoid Malignancies with Non-myeloablative Stem Cell Transplantation

2002· review· en· W2065719321 on OpenAlexaff
Matthew D. Seftel, Jessie R. Lavoie, Michael J. Barnett, Eibhlin Conneally

Bibliographic record

VenueHematology · 2002
Typereview
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsVancouver Hospital and Health Sciences CentreUniversity of British ColumbiaBC Cancer Agency
Fundersnot available
KeywordsTransplantationStem cellMedicineHematopoietic stem cell transplantationOncologyRegimenConditioning regimenInternal medicineBiology

Abstract

fetched live from OpenAlex

The traditional approach to allogeneic hematopoietic stem cell transplantation involves the administration of myeloablative preparative regimens. This form of conditioning is associated with a relatively high incidence of regimen-related toxicity. As a result, candidates for allogeneic stem cell transplantation may be excluded owing to advanced age or co-morbid medical illness. Recently, so-called "non-myeloablative" regimens have been introduced, where less intense conditioning therapy is used in an attempt to reduce regimen-related toxicity. In addition, non-myeloablative transplantation takes advantage of the graft-versus-tumour effect that is characteristic of allogeneic stem cell transplantation. We review the background, available clinical data, and future directions in non-myeloablative stem cell transplantation, and focus on its potential use in the treatment of lymphoid malignancies.

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)0.000
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.040
GPT teacher head0.294
Teacher spread0.254 · 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

Citations1
Published2002
Admission routes1
Has abstractyes

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