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Record W2541662183 · doi:10.3747/co.23.3205

Predictors of Adjuvant Treatment for Pancreatic Adenocarcinoma at the Population Level

2016· article· en· W2541662183 on OpenAlexafffundvenueabout
Daniel J. Kagedan, Matthew Dixon, Reshma Raju, Q. Li, Maryam Elmi, Eun-Kyung Shin, Ning Liu, Abraham El‐Sedfy, Lawrence Paszat, Alexander Kiss, Craig C. Earle, Nicole Mittmann, Natalie G. Coburn

Bibliographic record

VenueCurrent Oncology · 2016
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsHealth Sciences CentreInstitute for Clinical Evaluative SciencesSunnybrook Health Science CentreUniversity of Toronto
FundersInstitute for Clinical Evaluative SciencesCancer Research SocietyOntario Ministry of Health and Long-Term CareJohns Hopkins University
KeywordsMedicineInternal medicinePopulationAdjuvantCohortPancreatic cancerLogistic regressionAdjuvant therapyOncologyCancer

Abstract

fetched live from OpenAlex

Background: In the present study, we aimed to describe, at the population level, patterns of adjuvant treatment use after curative-intent resection for pancreatic adenocarcinoma (PCC) and to identify independent predictors of adjuvant treatment use. Methods: In this observational cohort study, patients undergoing PCC resection in the province of Ontario (population 13 million) during 2005–2010 were identified using the provincial cancer registry and were linked to administrative databases that include all treatments received and outcomes experienced in the province. Patients were defined as having received chemotherapy (CTX), chemoradiation (CRT), or observation (OBS). Clinicopathologic factors associated with the use of CTX, CRT, or OBS were identified by chi-square test. Logistic regression analyses were used to identify independent predictors of adjuvant treatment versus OBS, and CTX versus CRT. Results: Of the 397 patients included, 75.3% received adjuvant treatment (27.2% CRT, 48.1% CTX) and 24.7% received obs. Within a single-payer health care system with universal coverage of costs for CTX and CRT, substantial variation by geographic region was observed. Although the likelihood of receiving adjuvant treatment increased from 2005 to 2010 (p = 0.002), multivariate analysis revealed widespread variation between the treating hospitals (p = 0.001), and even between high-volume hepatopancreatobiliary hospitals (p = 0.0006). Younger age, positive lymph nodes, and positive surgical resection margins predicted an increased likelihood of receiving adjuvant treatment. Among patients receiving adjuvant treatment, positive margins and a low comorbidity burden were associated with CRT compared with CTX. Conclusions: Interinstitutional medical practice variation contributes significantly to differential patterns in the rate of adjuvant treatment for PCC. Whether such variation is warranted or unwarranted requires further investigation.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.091
Threshold uncertainty score0.181

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.238
GPT teacher head0.448
Teacher spread0.210 · 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

Citations13
Published2016
Admission routes4
Has abstractyes

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