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Record W2079335457 · doi:10.1080/10428190902756578

Incidence and risk factors for second cancers after autologous hematopoietic cell transplantation for aggressive non-Hodgkin lymphoma

2009· article· en· W2079335457 on OpenAlexafffund
Tara Seshadri, Melania Pintilie, John Kuruvilla, Armand Keating, Richard Tsang, Sahar Zadeh, Michael Crump

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2009
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
FundersUniversity Health Network
KeywordsMedicineInternal medicineEtoposideTransplantationMelphalanChemotherapyOncologyLymphomaCumulative incidenceCancerTotal body irradiationPopulationHematopoietic stem cell transplantationSurgeryCyclophosphamide

Abstract

fetched live from OpenAlex

Autologous hematopoietic stem cell transplantation (AHCT) for relapsed/refractory aggressive non-Hodgkin lymphoma (NHL) results in long-term disease-free survival in 40-50% of patients. The incidence of and risk factors for second cancer development in these patients have not been well studied. We analysed 372 patients with relapsed/refractory aggressive NHL who underwent AHCT from 1987 to 2006. Median age at AHCT was 50 years (range 19-70). Most patients (74%) received two chemotherapy regimens before transplant. High-dose chemotherapy consisted of etoposide and melphalan in 95% of patients and 16% received total body irradiation. Thirty-two patients (9%) developed a second cancer (19 hematologic, 13 solid tumors). The probability of second cancer at 3 and 10 years post-AHCT was 4.4% and 12.9%, respectively. When compared with the general population, the relative-risk of acute myeloid leukemia and new solid tumor was 13.2 (p < 0.0001) and 2.3 (p = 0.0013). Salvage therapy using mini-BEAM was significantly associated with second cancer development (p = 0.004). In conclusion, second cancers are a significant cause of late morbidity and mortality patients treated with AHCT with curative intent, and appear increased in patients exposed to mini-BEAM chemotherapy.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.007
GPT teacher head0.241
Teacher spread0.235 · 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.

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

Citations33
Published2009
Admission routes2
Has abstractyes

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