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Record W2990548197 · doi:10.1016/j.cct.2019.105898

Web-based cognitive-behavioral intervention for pain in pediatric acute recurrent and chronic pancreatitis: Protocol of a multicenter randomized controlled trial from the study of chronic pancreatitis, diabetes and pancreatic cancer (CPDPC)

2019· article· en· W2990548197 on OpenAlexaff
Tonya M. Palermo, Caitlin B. Murray, Homer Aalfs, Maisam Abu‐El‐Haija, Bradley Barth, Melena D. Bellin, Kate Ellery, Douglas S. Fishman, Cheryl E. Gariepy, Matthew J. Giefer, Praveen S. Goday, Tanja Gonska, Melvin B. Heyman, Sohail Z. Husain, Tom K. Lin, Quin Y. Liu, Maria R. Mascarenhas, Asim Maqbool, Brian A. McFerron, Véronique D. Morinville, Jaimie D. Nathan, Chee Y. Ooi, Emily R. Perito, John F. Pohl, Sarah Jane Schwarzenberg, Zachary M. Sellers, José Serrano, Uzma Shah, David M. Troendle, Yuhua Zheng, Ying Yuan, Mark E. Lowe, Aliye Uç

Bibliographic record

VenueContemporary Clinical Trials · 2019
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcGill UniversityMontreal Children's HospitalHospital for Sick Children
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of Diabetes and Digestive and Kidney DiseasesNational Cancer Institute
KeywordsMedicinePancreatitisRandomized controlled trialPancreatic cancerIntervention (counseling)Pancreatitis, chronicChronic painInternal medicineDiabetes mellitusProtocol (science)Physical therapyCancerPsychiatryAlternative medicinePathology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.010
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: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.021
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.011
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0070.004
Bibliometrics0.0020.002
Science and technology studies0.0030.003
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0210.003

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 head0.101
GPT teacher head0.448
Teacher spread0.347 · 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 designRandomized trial
Domainnot available
GenreProtocol

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

Citations23
Published2019
Admission routes1
Has abstractno

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