Self-Management for People with Inflammatory Bowel Disease
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".