Evaluating class solutions: model fit criteria.
Bibliographic record
Abstract
<div><p>Background</p><p>Recent data have demonstrated that healthcare use after treatment for respiratory tuberculosis (TB) remains elevated in the years following treatment completion. However, it remains unclear which TB survivors are high healthcare users and whether any variation exists within this population. Thus, the primary objective of this study was to identify distinct profiles of high healthcare-use TB survivors to help inform post-treatment support and care.</p><p>Methods</p><p>Using linked health administrative data from British Columbia, Canada, we identified foreign-born individuals who completed treatment for incident respiratory TB between 1990 and 2019. We defined high healthcare-use TB survivors as those in the top 10% of annual emergency department visits, hospital admissions, or general practitioner visits among the study population during the five-year period immediately following TB treatment completion. We then used latent class analysis to categorize the identified high healthcare-use TB survivors into subgroups.</p><p>Results</p><p>Of the 1,240 people who completed treatment for respiratory TB, 258 (20.8%) people were identified as high post- TB healthcare users. Latent class analysis results in a 2-class solution. Class 1 (n = 196; 76.0%) included older individuals (median age 71.0; IQR 59.8, 79.0) with a higher probability of pre-existing hypertension and diabetes (41.3% and 33.2%, respectively). Class 2 (n = 62; 24.0%) comprised of younger individuals (median age 31.0; IQR 27.0, 41.0) with a high probability (61.3%) of immigrating to Canada within five years of their TB diagnosis and a low probability (11.3%) of moderate to high continuity of primary care.</p><p>Discussion</p><p>Our findings suggest that foreign-born high healthcare-use TB survivors in a high-resource setting may be categorized into distinct profiles to help guide the development of person-centred care strategies targeting the long-term health impacts TB survivors face.</p></div>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".