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Patient Factors Associated with Enrollment on an Acute Myeloid Leukemia Phase III Clinical Trial: A Report from the Children’s Oncology Group

2014· article· en· W2579174581 on OpenAlexaff
Pooja Rao, Yimei Li, Todd A. Alonzo, Lillian Sung, Robert B. Gerbing, Kelly Getz, Alix E. Seif, Tamara P. Miller, Yuan‐Shung Huang, Rochelle Bagatell, Matt Hall, Brian T. Fisher, Alan S. Gamis, Richard Aplenc

Bibliographic record

VenueBlood · 2014
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsHospital for Sick Children
Fundersnot available
KeywordsMedicineCogClinical trialCohortInternal medicineLogistic regressionRetrospective cohort studyPediatricsOncology

Abstract

fetched live from OpenAlex

Abstract The increased survival of children with cancer is largely credited to treatment on clinical trials. As such there is interest in determining factors associated with trial enrollment. Adult oncology studies suggest non-Whites are less likely to enroll than Whites on clinical trials. Additional factors, such as insurance and geographic region, have been associated with adolescent and adult trial enrollment. Data describing factors associated with pediatric trial enrollment is limited. This study sought to evaluate whether race and other patient factors were associated with enrollment on the recently completed Children's Oncology Group (COG) acute myeloid leukemia (AML) chemotherapy trial AAML0531. A retrospective cohort study was conducted using data from the Pediatric Health Information System (PHIS) and trial data from COG AAML0531. PHIS is an administrative database containing inpatient data from Child Health Corporation of America-affiliated hospitals. Data from a previously assembled and validated PHIS AML cohort was merged with data from the AAML0531 trial for the period of time that the COG trial was open and PHIS data was available (2006 to 2010). Patients that were identified in both the PHIS and COG datasets were labeled as “enrolled patients” and those only in the PHIS database were labeled as “not enrolled patients”. Enrolled and not enrolled patients were compared by race, gender, age, insurance type, illness severity at AML presentation (defined by need for an ICU intervention in the first or second day of index admission) and geographic region of hospitalization. Chi-square test and logistic regression analyses were used for unadjusted and adjusted comparisons, respectively. The PHIS AML cohort contained 874 patients of which 312 (36%) were enrolled on AAML0531. Table 1 displays the comparison of enrolled versus not enrolled patients by each of the demographic and clinical variables of interest. In adjusted analyses patients were less likely to enroll if they had government insurance (compared to private insurance). Patients were more likely to enroll if they were hospitalized in the West (compared to the South). Trial enrollment percentage was higher than reported in adult cooperative group trials but still comprised a minority of potentially eligible patients. Unlike adult clinical trials, race was not associated with enrollment on the AAML0531 trial. However, patients who did enroll on AAML0531 were less likely to have government insurance and more likely to be hospitalized in the West. Work is ongoing to refine estimates of enrollment eligible patients, to define patient socio-economic status more precisely than insurance status, and to evaluate the impact of insurance status beyond socio-economic status. Such analyses should provide additional data to guide efforts to increase trial enrollment and ensure equitable access to COG AML clinical trials for all pediatric patients. 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)0.002
metaresearch head score (Gemma)0.011
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.050
GPT teacher head0.366
Teacher spread0.316 · 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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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