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Record W2039142540 · doi:10.1002/pbc.22190

The Quid Pro Quo of pediatric versus adult services for older adolescent cancer patients

2009· article· en· W2039142540 on OpenAlexaff
Archie Bleyer

Bibliographic record

VenuePediatric Blood & Cancer · 2009
Typearticle
Languageen
FieldMedicine
TopicChildhood Cancer Survivors' Quality of Life
Canadian institutionsChild, Adolescent and Family Mental Health
FundersAflac
KeywordsMedicineCogCancerBlood cancerPediatric cancerPediatric oncologyRegimenInternal medicineOncologyPediatrics

Abstract

fetched live from OpenAlex

PURPOSE: Data from the State of Georgia suggest that pediatric cancers have better survival outcomes when treated at pediatric cancer centers that are members of the nation's Children's Oncology Group (COG). PATIENTS AND METHODS: To determine if the more adult types of cancer that occur in adolescents are better treated at centers with adult oncology expertise, the reported data were re-analyzed according to a scale that assessed whether the type of cancer was more likely to have been treated by oncologists with pediatric versus adult cancer experience. RESULTS: The results showed that survival hazard index was linearly correlated in 15- to 19-year-olds with the pediatric versus adult cancer type index (P < 0.0001). All of the five most pediatric type of cancers had a better survival at COG institutions and all of the three tumors with a better survival at non-COG institutions had the highest adult type scores. CONCLUSION: These results demonstrate that adolescent patients with pediatric types of cancer fare better when their care is conducted or supervised by oncologists who specialize in the care of their type of cancer. The Georgia data are among the first to indicate that the more adult type of cancers are better treated on an adult treatment regimen and/or under the supervision or in conjunction with adult-treating oncologists.

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)0.008
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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.019
GPT teacher head0.314
Teacher spread0.295 · 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

Citations48
Published2009
Admission routes1
Has abstractyes

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