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Record W2971786452 · doi:10.1155/2019/7135437

Up-to-Date Tailored Systemic Treatment in Pancreatic Ductal Adenocarcinoma

2019· review· en· W2971786452 on OpenAlexfundno aff
Caroline Fong, Emma Burke, David Cunningham, Naureen Starling

Bibliographic record

VenueGastroenterology Research and Practice · 2019
Typereview
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsnot available
FundersServierNational Institute for Health and Care ResearchSanofiMedImmuneMerckRoyal Marsden NHS Foundation TrustAmgenPfizerInstitute of Cancer ResearchCelgeneBayerAstraZenecaEli Lilly and CompanyBristol-Myers Squibb
KeywordsMedicineMalignancyPancreatic ductal adenocarcinomaStromaLimitingImmunotherapyOncologyPancreatic cancerTumor microenvironmentIntensive care medicineInternal medicineCancer

Abstract

fetched live from OpenAlex

Despite intensive research efforts, pancreatic ductal adenocarcinoma is still regarded as an aggressive and life-limiting malignancy. Combination chemotherapy regimens that underpin the current treatment approach in the advanced setting have led to incremental survival gains in recent years but have failed to confer patients with a median overall survival that exceeds 12 months from diagnosis. Research has since focussed on understanding the role and interplay between various components of the desmoplastic stroma and tumour microenvironment, in addition to developing targeted therapies based on molecular features to improve the prognosis associated with this malignancy. This review will summarise the available systemic treatment options and discuss potential methods to refine the resolution of patient selection to enhance responses to currently available therapies. Furthermore, it will explore newer approaches anticipated to come to the fore of future clinical practice, such as agents targeting the DNA damage response and tumour microenvironment as well as immunotherapy-based combinations.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.236
GPT teacher head0.489
Teacher spread0.253 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations10
Published2019
Admission routes1
Has abstractyes

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