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Record W4211212008 · doi:10.1002/ueg2.12209

The use of a smartphone application to disseminate guidelines on pancreatic cystic neoplasms

2022· article· en· W4211212008 on OpenAlexaff
Alberto Balduzzi, Giovanni Marchegiani, Marco Zampese, Roberto Salvia

Bibliographic record

VenueUnited European Gastroenterology Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsPancreas Centre (Canada)
Fundersnot available
KeywordsMedicineClinical PracticeBest practiceHealth careFamily medicineManagementPolitical science

Abstract

fetched live from OpenAlex

Officially release in October 2019, iCyst was developed as part of the project entitled “Current application of the European evidence‐based guidelines on pancreatic cystic tumors”, which was promoted by the Department of General and pancreatic Surgery – The Pancreas Institute, University of Verona Hospital Trust (Institutional Review Board approval number 2390CESC – Comitato Etico delle Province di Verona e Rovigo), and received funding from the United European Gastroenterology Activity Grants – Support of Standards & Guidelines initiatives, dissemination of existing clinical practice 2019 (endorse by the European Digestive Surgery – EDS).

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)0.010
metaresearch head score (Gemma)0.054
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.102

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.054
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.001
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0300.018

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 head0.058
GPT teacher head0.328
Teacher spread0.270 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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