Virtual assessment and management of chronic cough: A scoping review
Bibliographic record
Abstract
Background: Chronic cough is a frequent reason for seeking consultation with primary care providers. The recent widespread adoption of virtual care offers a promising alternative that can be used to optimize the assessment and management of this condition. The objective of this review was to map and identify the strategies used to assess and/or manage chronic cough virtually, and to explore their impact on cough severity and patient satisfaction with virtual care. Methods: A scoping review was conducted in MEDLINE, EMBASE, and CINAHL in May 2023. Research questions were defined based on the Population, Concept, Context mnemonic, and literature search was conducted using a three-step approach. Study selection involved the steps of identification, screening, eligibility, and inclusion. A descriptive synthesis was performed, and quantitative variables were presented as absolute and relative frequencies. Results: A total of 4953 studies were identified and seven met the inclusion criteria. The following mHealth and telehealth strategies were identified: diagnostic website, specialized online clinic, online speech language therapy, and remote follow-up to assess the effectiveness of in-person interventions. Results indicated that these virtual strategies can be useful to assess chronic cough, treat, and track chronic cough symptoms. Overall, patients were satisfied with the approaches. Conclusion: Although literature is scarce, evidence suggests that virtual strategies for the assessment and management of chronic cough may be effective and are well-received by patients. However, further research is needed to identify the type and characteristics of virtual approaches leading to optimize and facilitate the care of patients with this condition. This will also help develop a strong body of evidence to support their incorporation into guidelines and clinical practice.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".