The Canadian Cohort Obstructive Lung Disease (CanCOLD): New Insights for Primary Care Application
Bibliographic record
Abstract
The Canadian Cohort Obstructive Lung disease (CanCOLD) study was launched ten years ago, to provide insight on the natural course of chronic obstructive pulmonary disease (COPD).This project, unique on the world scene for its longitudinal evaluation, is based on random sampling of the population as opposed to more traditional cohorts built on convenience samples of clinical patients.This comprehensive review article has elected to extract and collate from the set of Can-COLD substudies those findings and emerging discoveries that can serve to influence clinical practice, in particular, for primary care.We are presenting findings grouped around three aspects: prevalence of COPD, diagnostic performance, and impacts of COPD.Furthermore, the main results of these studies are supplemented by key messages.From CanCOLD publications we have already learned much, with sufficient substantive data to warrant translation into our clinical practice, guidelines and health policies for the benefit of our COPD patients.(BRN Rev. 2019;5(4):249-62)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.045 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".