How well can we practically apply asthma guidelines?
Bibliographic record
Abstract
Much effort is expended to create Asthma clinical practice guidelines (CPGs) which compile the best evidence currently available. Their implementation is limited by complexity, lack of clarity, and failure to address various practical aspects of daily patient management. We analyzed pharmacotherapy recommendations in the latest Canadian Thoracic Society (CTS) asthma CPG (2010 guidelines/2012 update) from the primary care perspective, in order to identify practice-based gaps. We reviewed therapeutic initiation, escalation, and de-escalation in adults, and identified questions which arose when attempting to implement recommendations practically. We also searched for guidance provided by other recent international asthma guidelines (BTS/SIGN and GINA) in these specific areas. The following three clinical questions arose from the CTS guideline (where recommendations were made but provided insufficient guidance): 1) when to initiate controller therapy; 2) how long after treatment initiation or escalation to evaluate for effect; and 3) when and how to de-escalate therapy when control is established. We also attempted to create a set of decision rules for therapeutic escalation and de-escalation amongst currently available asthma therapies. Though some of these issues addressed in other guidelines, none provided practical guidance as to how to manage all possible clinical scenarios. Day to day clinical dilemmas are often not explicitly addressed in the current CPGs which limits their practical implementability in daily patient care. One potential solution to this issue would be the active involvement of primary care end-users in the guideline writing process to help authors to identify these areas at the guideline development stage.
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 | 0.237 | 0.587 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.032 | 0.032 |
| Open science | 0.009 | 0.011 |
| Research integrity | 0.022 | 0.027 |
| Insufficient payload (model declined to judge) | 0.009 | 0.008 |
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".