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Record W18458285 · doi:10.1155/2008/428967

Self-Management for People with Inflammatory Bowel Disease

2008· review· en· W18458285 on OpenAlexaffvenueabout
Fred Saibil, Emily Lai, Andrew Hayward, Jeanne Yip, Cameron Gilbert

Bibliographic record

VenueCanadian Journal of Gastroenterology · 2008
Typereview
Languageen
FieldHealth Professions
TopicAdolescent and Pediatric Healthcare
Canadian institutionsUniversity of OttawaHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineInflammatory bowel diseaseDiseaseSelf-managementAsthmaIntensive care medicineEconomic shortagePopulationChronic diseasePulmonary diseaseDisease managementFamily medicineImmunologyEnvironmental healthPathologyComputer scienceInternal medicine

Abstract

fetched live from OpenAlex

In North America and the United Kingdom, we are in the age of self-management. Many patients with chronic diseases are ready to participate in the therapeutic decision-making process, and join their physicians in a co-management model. It is particularly useful to consider this concept at a time when physician shortages and waiting times are on the front page every day, with no immediate prospect of relief. Conditions such as diabetes, asthma, chronic obstructive pulmonary disease, recurrent urinary tract infections and others lend themselves to this paradigm of medical care for the informed patient. The present paper reviews some of the literature on self-management for the patient with inflammatory bowel disease (IBD), and provides a framework for the use of self-management in the IBD population, with emphasis on the concept of a patient passport, and the use of e-mail, supported by an e-mail contract, as proposed by the Canadian Medical Protective Association. Examples of specific management strategies are provided for several different IBD scenarios. Eliminating the need for some office visits has clear environmental and economical benefits. Potential negative consequences of this form of patient care are also discussed.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.694
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.036
GPT teacher head0.349
Teacher spread0.313 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations36
Published2008
Admission routes3
Has abstractyes

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