Bibliographic record
Abstract
Background Chronic Obstructive Pulmonary Disease (COPD) afflicts one in five New Brunswick (NB), Canada residents aged 65+. The NB Institute for Research, Data and Training (NB-IRDT) has undertaken a project to develop an integrated COPD health information platform (CHIP) to study COPD in the NB population. Aims The aims of the CHIP initiative are 1) identification and tracking of population level diagnoses of COPD in NB, and 2) advancement in the management of COPD at the system planning, research, clinical practice and patient levels. Approach Unique in Canada, CHIP combines clinical and administrative data from multiple sources including all NB pulmonary function test laboratories (PFT). Clinics collect lung function test results, sociodemographic information, smoking and cessation, height, weight and other fields on everyone tested at a PFT. Since PFT data are not part of the NB electronic health record, data had to be assembled at each clinic. Data transfer required data sharing agreements, privacy impact assessments and disclosure schedules among Provincial Health Authorities, Department of Health and the University of New Brunswick. Results Clinical data from 2007-2017 have been standardized and transferred to NB-IRDT from all PFT clinics in NB. More than 100,000 tests have been linked to patient data on hospitalizations, physician visits, vital statistics, and prescriptions. A research working group of researchers, clinicians, administrators and patients has identified key questions that are being analyzed using the CHIP data platform, including the extent to which individuals with COPD tested at a PFT are receiving appropriate treatment, and whether individuals treated for COPD have been tested. Conclusion CHIP is a valuable tool to support research on COPD. Through identification of predictors and outcomes of COPD, CHIP will lead to improved prevention and treatment for NB residents and will generate valuable information transferrable to other jurisdictions.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.043 |
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".