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Identification of the CD34 enumeration on the day before stem cell harvest that best predicts poor mobilization

2010· article· en· W1805804819 on OpenAlexaff
David Szwajcer, Alana Jennings‐Coutts, Angeline Giftakis, Donna A. Wall

Bibliographic record

VenueTransfusion · 2010
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsCancerCare Manitoba
Fundersnot available
KeywordsMedicineMobilizationCD34Multiple myelomaPeripheral bloodPopulationOdds ratioInternal medicineStem cellBiology

Abstract

fetched live from OpenAlex

BACKGROUND: We questioned whether CD34 enumeration in peripheral blood on the day before planned collection would identify a patient population that could benefit from an augmented collection strategy during that mobilization attempt. STUDY DESIGN AND METHODS: A retrospective review of all adult patients who underwent a first mobilization attempt between January and December 2008 for autologous use was undertaken. Peripheral blood CD34 quantitation on the day before planned collection (Day -1) and day of planned collection (Day 0) was correlated with likelihood of a successful collection. RESULTS: Of 41 patients (15 with multiple myeloma, 20 with lymphoma, and six with other malignancies) who underwent mobilization 24 patients (58%) were harvested in 1 day (good mobilizers) with the remaining 17 patients (42%) either requiring more than 1 day to collect or were not collected (poor mobilizers [PMs]). A peripheral blood CD34+ count below 10 × 10(6) /L on Day -1 was optimal in identifying PMs (adjusted odds ratio of 7 [1.4-35]). Increasing the CD34 cutoff from 10 × 10(6) to 15 × 10(6) /L decreased the prediction of poor mobilization (positive likelihood ratio dropped from 3.3 to 2.2). CONCLUSION: Peripheral blood CD34 content of less than or equal to 10 × 10(6) CD34+ cells/L on the day before collection is predictive of poor mobilization whereas higher peripheral blood CD34 counts on Day -1 have a high likelihood of successful 1-day collection.

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)0.001
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 score0.077
Threshold uncertainty score0.365

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.240
Teacher spread0.223 · 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

Citations17
Published2010
Admission routes1
Has abstractyes

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