Informing community pharmacists on COPD case-finding methods: A scoping review
Bibliographic record
Abstract
Background: Early detection of chronic obstructive pulmonary disease (COPD) is a strategy to address the increasing human and economic costs of this condition. This study aimed to inform pharmacists' case-finding strategies by providing an overview of case-finding approaches by health care practitioners. Methods: A scoping review was conducted based on the Joanna Briggs Institute and the PRISMA Extension for Scoping Reviews (PRISMA ScR) guidelines. Included studies were analyzed under the following themes: population characteristics, inclusion and exclusion criteria, setting, case-finding strategies and yield, health care practitioners involved, interprofessional collaboration and the provision of preventive services. Studies were then characterized by highest yields (the weighted average of each approach expressed as a percentage of the total number of new COPD cases divided by the total number of patients screened using the same approach). Results: The screening process produced 170 eligible studies. Twenty case-finding approaches with average yields of new COPD cases ranging from 3.8% to 29% were identified. The approach with the highest yield involved the use of a questionnaire, peak flow meter and pre-post spirometry. In 14 of these approaches, the process was initiated with the use of questionnaires. In many of the studies, case-finding was targeted at high-risk and/or symptomatic patients. Characteristics of studies with the highest case-finding yields included provider education/training, patient education, active screening, multistep approaches to case-finding, provider engagement, diagnostic criteria by guidelines and engagement of other health care practitioners. Conclusion: In our scoping review of case-finding methods for patients with COPD, we found the greatest yield from pre-post spirometry following initial screening with a peak flow meter and questionnaire. Pharmacists and health researchers can use these approaches to identify high-risk patients for interventions.
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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 | 0.183 | 0.365 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.040 | 0.030 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".