MétaCan
Menu
Back to cohort
Record W2069827448 · doi:10.1182/blood-2004-04-1363

p53 gene deletion detected by fluorescence in situ hybridization is an adverse prognostic factor for patients with multiple myeloma following autologous stem cell transplantation

2004· article· en· W2069827448 on OpenAlexaff
Hong Chang, Connie Qi, Qi-Long Yi, Donna Reece, A. Keith Stewart

Bibliographic record

VenueBlood · 2004
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsMultiple myelomaFluorescence in situ hybridizationAutologous stem-cell transplantationTransplantationBiologyInternal medicineBeta-2 microglobulinChemotherapyStem cellOncologyCancer researchPathologyImmunologyMedicineGastroenterologyGene

Abstract

fetched live from OpenAlex

We investigated the relevance of p53 deletions to the clinical outcome of patients with multiple myeloma (MM) treated with high-dose chemotherapy and autologous stem cell transplantation. Hemizygous p53 gene deletions were detected by fluorescence in situ hybridization in 10 of 105 (9.5%) patients studied. p53 deletions were associated with higher serum calcium (P = .0062) and creatinine (P = .013) levels, but there were no association with patient age, gender, beta2-microglobulin, C-reactive protein, hemoglobin, albumin or bone lytic lesions, or immunoglobulin isotype. There were no associations of p53 deletions with 13q deletions or translocations t(11;14) or t(4;14). Patients with p53 deletions had significantly shorter progression-free (median, 7.9 versus 25.7 months, P = .0324) and overall survival (median, 14.7 versus 48.1 months, P = .0008) than patients without a p53 deletion. A multivariate analysis confirmed p53 deletion was an independent prognostic factor predicting shortened progression-free (P = .0009) or overall survival (P = .0002) in patients with MM after high-dose chemotherapy and autologous stem cell transplantation.

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 score0.193
Threshold uncertainty score0.647

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 head0.012
GPT teacher head0.238
Teacher spread0.227 · 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

Citations202
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueBloodSame topicMultiple Myeloma Research and TreatmentsFrench-language works237,207