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Predictors Of Delay In Diagnosis and Treatment In Diffuse Large B-Cell Lymphoma and Impact On Survival

2013· article· en· W2289506799 on OpenAlexaffabout
Anna S. Nikonova, Rena Buckstein, Hany Giurgis, Matthew C. Cheung

Bibliographic record

VenueBlood · 2013
Typearticle
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsHealth Sciences CentreSunnybrook Health Science CentreUniversity of Toronto
Fundersnot available
KeywordsMedicineHematologyInternal medicineRituximabVincristineDiffuse large B-cell lymphomaPrednisoneCyclophosphamidePopulationChemotherapy regimenChemotherapyLymphomaOncologySurgery

Abstract

fetched live from OpenAlex

Abstract Background Although diagnostic and treatment delays in solid tumors are known to negatively impact on outcomes, little is known with respect to hematological malignancies. Diffuse Large B-Cell Lymphoma may present with a wide array of symptoms, thus rendering initial diagnosis challenging and time consuming. We evaluated disease-specific, patient-related and socioeconomic factors leading to delays in DLBCL diagnosis and treatment and the respective impact on overall and progression-free survival. Methods A comprehensive clinical database of patients with a new diagnosis or new presentation of transformed DLBCL treated at our center between 2002 and 2010 was utilized. A total 278 patients were included. All patients received at least one cycle of Rituximab, Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone (R-CHOP) immuno-chemotherapy. We defined various time intervals based on Cancer Care Ontario guidelines as follows: patient associated delay – time from symptoms onset to first known contact with a primary care physician (PCP); diagnostic delay – >6 weeks from first PCP contact to initial hematology consultation; and treatment delay – >4 weeks from first hematology consultation to chemotherapy initiation. Results In the population studied (n=278), the median age was 63 and 46% were female. Patients waited a median of 4 weeks (IQR 2-13) before seeking medical attention. A further median of 8 weeks (IQR 4-17) was required for the PCP to diagnose DLBCL or at least to achieve enough clinical suspicion for referral to hematology. From initial hematology consult, a median of 3 weeks (IQR 1-4) elapsed until chemotherapy initiation. In univariate analyses, patients who lacked bone marrow involvement (p=.005), had lower IPI scores (p=.031), higher Charlson comorbidity index (p=.048) and who had initiation of treatment in the outpatient setting (vs. inpatient; p=.021), were more likely to experience diagnostic delays >6 weeks. In multivariable logistic regression analysis, bone marrow involvement (OR=0.41, p=.018), Charlson comorbidity index (OR=1.42, p=.017) and requirement for urgent inpatient chemotherapy administration (OR=0.40, p=.012) remained associated with diagnostic delays. With respect to treatment delays, in univariate analyses, patients who did not have a pathology diagnosis at the time of initial hematology consultation (p<.0001) and those with B symptoms (p=.039) were more likely to experience treatment delays >4 weeks. On multivariable analysis, lack of pathological diagnosis at the time of hematology referral was the only factor that remained associated with treatment delays (OR=8.25, p<0.001). No socioeconomic factors (low income, level of education, and cohabiting alone) predicted for either diagnostic or treatment delays. On Cox multivariable regression analyses, diagnostic (Fig 1) or treatment delays (Fig 2) did not impact on survival or progression-free survival; only IPI score and number of R-CHOP cycles significantly impacted overall survival (HR=1.82, p<.001; HR=0.70 p<.001) and progression-free survival (HR=1.56, p<.001; HR=0.82, p=.004). Conclusion Selected disease and patient-related factors may be associated with delays in management of DLBCL. However, unlike in solid tumor presentations, we can reassure patients that waiting a reasonable time frame to complete diagnostic and staging milestones should not affect their disease course, as long as appropriate chemotherapy dosing is administered. Disclosures: No relevant conflicts of interest to declare.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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 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".

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Citations2
Published2013
Admission routes2
Has abstractyes

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