Health service utilization in IBD: comparison of self-report and administrative data
Bibliographic record
Abstract
BACKGROUND: The reliability of self-report regarding health care utilization in inflammatory bowel disease (IBD) is unknown. If proven reliable, it could help justify self-report as a means of determining health care utilization and associated costs. METHODS: The Manitoba IBD Cohort Study is a population-based longitudinal study of participants diagnosed within 7 years of enrollment. Health care utilization was assessed through standardized interview. Participants (n = 352) reported the total number of nights hospitalized, frequency of physician contacts in the prior 12 months and whether the medical contacts were for IBD-related reasons or not. Reports of recent antibiotic use were also recorded. Actual utilization was drawn from the administrative database of Manitoba Health, the single comprehensive provincial health insurer. RESULTS: According to the administrative data, 15% of respondents had an overnight hospitalization, while 10% had an IBD-related hospitalization. Self-report concordance was highly sensitive (92%; 82%) and specific (96%; 97%, respectively). 97% of participants had contact with a physician in the previous year, and 69% had IBD-related visits. Physician visits were significantly under-reported and there was a trend to over-report the number of nights in hospital. CONCLUSIONS: Self-report data can be helpful in evaluating health service utilization, provided that the researcher is aware of the systematic sources of bias. Outpatient visits are well identified by self-report. The discordance for the type of outpatient visit may be either a weakness of self-report or a flaw in diagnosis coding of the administrative data. If administrative data are not available, self-report information may be a cost-effective alternative, particularly for hospitalizations.
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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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".