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Record W2921409394 · doi:10.1093/jcag/gwz006.222

A223 OPTIMIZING CO-MANAGEMENT OF PANCREATIC ENZYME INSUFFICIENCY: A CANADIAN CONSENSUS

2019· article· en· W2921409394 on OpenAlexaffabout
Fergal Donnellan, Dany Baillargeon, Christopher Teshima

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2019
Typearticle
Languageen
FieldMedicine
TopicPancreatitis Pathology and Treatment
Canadian institutionsUniversity of TorontoUniversité de SherbrookeVancouver General Hospital
Fundersnot available
KeywordsMedicineReferralPancreatitisEnzyme replacement therapyExocrine pancreatic insufficiencyDiseasePancreatic enzymesNewborn screeningIntensive care medicineCystic fibrosisInternal medicinePediatricsFamily medicine

Abstract

fetched live from OpenAlex

Pancreatic enzyme insufficiency (PEI) arises from a variety of causes in various pathologic states, including inborn disease (notably cystic fibrosis (CF)); pancreatic duct obstruction, e.g., due to local tumour growth; and chronic pancreatitis (CP). PEI can therefore arise at any time in life, from infancy to advanced old age, and it presents clinically with a relatively non-specific constellation of gastrointestinal (GI) symptoms and laboratory findings, all associated with maldigestion and malabsorption. Signs and symptoms of PEI can be managed with pancreatic enzyme replacement therapy (PERT). For most forms of PEI, primary care physicians (PCPs) are likely to be the first to identify these signs and symptoms and to make a preliminary diagnosis. A Canadian Expert Panel has recently published a consensus guidance document on PEI diagnosis and management. The Panel’s recommendations have clear implications for PCPs who refer patients with suspected PEI or who need to co-manage patients with this condition. Here, we describe and comment on the Panel’s recommendations that guide GI specialists’ interactions with PCPs. Key questions about PEI were identified by the Panel and researched in the clinical literature. The Panel reviewed the findings and developed consensus recommendations related to: diagnostic features of PEI; conditions that increase the index of suspicion of PEI; screening of patients considered at elevated risk of PEI; investigations that should be carried out at the PCP level and communicated to the GI specialist via a referral letter; principles of PERT and PERT optimization; dietary and lifestyle advice for patients with PEI. Key diagnostic features of PEI include steatorrhea, unexplained weight loss, and specific micronutrient deficiencies. Predisposing conditions include CP, CF, type 1 diabetes, HIV/AIDS, celiac disease, and surgical resection in or near the pancreas. Other causes, including pancreatic cancer, may warrant urgent specialist referral for investigation. Patients with PEI typically should not be encouraged to reduce or avoid dietary fat but should be treated to improve their tolerance of a normal diet. Trial use of PERT may be considered by PCPs, particularly if wait time for GI specialist access is long. Response to treatment, along with other relevant aspects of patient history, should be communicated at the time of referral. Educational efforts should be directed to both GI specialists and PCPs to facilitate co-management of patients with confirmed or suspected PEI. This manuscript was funded by BGP Pharma ULC.

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)0.030
metaresearch head score (Gemma)0.053
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.437
Threshold uncertainty score0.878

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.053
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0040.004
Science and technology studies0.0080.003
Scholarly communication0.0050.004
Open science0.0080.007
Research integrity0.0160.017
Insufficient payload (model declined to judge)0.0090.004

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 head0.009
GPT teacher head0.235
Teacher spread0.226 · 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 designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2019
Admission routes2
Has abstractyes

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