A173 WHAT HAS CHANGED IN THE DIAGNOSIS OF CELIAC DISEASE IN CANADIANS OVER THE PAST 25 YEARS? RESULTS FROM THE STATE OF CELIAC IN CANADA SURVEY 2022
Bibliographic record
Abstract
Abstract Background Prior surveys of Canadians with celiac disease (CD) have demonstrated several challenges on the path to diagnosis. It has been over a decade since findings from the most recent survey were published. Aims To compare the experiences of Canadian individuals prior to CD diagnosis across time. Methods The State of Celiac Disease in Canada Survey is an online, nationwide survey distributed by Celiac Canada in December 2022. Over 75 questions were included, covering diagnostic pathways, presenting symptoms, and involvement of health care professionals (HCPs). Analysis was restricted to those who responded to having CD, dermatitis herpetiformis, or gluten ataxia, as well as provided the time since their diagnosis. Results A total of 6052 participants were included: 1210 (20.0%) were diagnosed in the last two years, 1113 (18.4%) two to five years ago, 1136 (18.8%) five to 10 years ago, 1040 (17.2%) 10 to 15 years ago, 960 (15.9%) 15 to 25 years ago, and 593 (9.8%) more than 25 years ago. Diarrhea was reported in over 70% of respondents diagnosed over 25 years ago, compared to approximately 50% diagnosed in the last five years (Figure 1A). Conversely, 31% of individuals diagnosed over 25 years ago experienced brain fog compared to 57% of those diagnosed in the last two years. Approximately 25% of respondents diagnosed at least 15 years ago experienced symptoms for more than 20 years prior, compared to 15% of those diagnosed in the last two years (Table 1). There were no clear differences in the proportion of respondents who saw a family physician before diagnosis, but there was a drop in those consulting a gastroenterologist (60% diagnosed more than 25 years ago versus 43% diagnosed in the last five years, Figure 1B). Conclusions Over time, there has been a shift in the clinical presentation of celiac disease, which may reflect improved detection of cases without gastrointestinal symptoms. Delays in diagnosis remain a concern but have also improved over time. The phenomenon of fewer patients consulting with gastroenterologists requires further investigation to understand the cause and impact on accurate diagnosis. Delays in diagnosis across time since diagnosis Note: 496 respondents did not report time from symptoms to diagnosis Symptoms experienced (A) and healthcare professionals consulted (B) prior to diagnosis Funding Agencies Celiac Canada (FKA Canadian Celiac Association)
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 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) | 0.003 | 0.000 |
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.
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".