Assessing Complexity Among Patients With Tuberculosis in California, 1993–2016
Bibliographic record
Abstract
BACKGROUND: Although the number of patients with active tuberculosis (TB) has decreased in the last 25 years, anecdotal reports suggest that the complexity of these patients has increased. However, this complexity and its components have never been quantified or defined. We therefore aimed to describe the complexity of patients with active TB in California during 1993-2016. METHODS: We analyzed data on patient comorbidities, clinical features, and demographics from the California Department of Public Health TB Registry. All adult patients who were alive at the time of TB diagnosis in California during 1993-2016 were included in the analyses. Factors deemed by an expert panel to increase complexity (ie, increased resources or expertise requirement for successful management) were analyzed and included the following: age >75 years, HIV infection, multidrug resistance (MDR), and extrapulmonary TB disease. Second, using additional information on other comorbidities available starting in 2010, we performed exploratory factor analysis on 25 variables in order to define the dimensions of complexity. RESULTS: < .001) from 1993 to 2016. Dimensions of complexity identified in the exploratory factor analysis included the following: race/immigration, social features, elderly/institutionalized, advanced TB, comorbidity, and drug resistance risk. CONCLUSIONS: In this first description of complexity in the setting of TB, we found that the complexity of patients with active TB has risen over the last 25 years in California. These findings suggest that despite the overall decline in active TB cases, effective management of more complex patients may require additional attention and resource investment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".