77 Inhalers for COPD have a measurable effect but do they really work?
Bibliographic record
Abstract
<h3></h3> Currently more than 20 inhalers are approved for the management of COPD and marketed in North America. About half are single medication inhalers and half are combination medication inhalers. This abundance of inhalers leaves many clinicians and patients confused and wondering which inhaler, if any, to add and how to make that decision. When making decisions about inhalers, clinicians should focus on the best available evidence around outcomes which are clinically relevant to patients. Although clinical trials may show statistically significant differences on clinically relevant outcomes, this doesn’t necessarily mean the magnitude of these differences would be considered clinically meaningful to an individual patient or clinician. To practice in an evidence-based fashion clinicians need to have an understanding of the ballpark chance an individual patient will get a clinically meaningful effect from any specific inhaler and, if possible identify if, in fact, a specific patient is getting a clinically important effect on their symptoms from a specific inhaler once it has been started. As of late 2018, multiple meta-analyses have been published outlining benefits and harms of triple therapy (LAMA/LABA/ICS) versus dual therapy (LAMA/LABA) in the management of COPD. In this session, using these meta-analyses and their individual clinical trial data, we’ll take an in-depth look at the magnitude of the benefits and harms attributed to these inhalers and the potential ways to use this evidence clinically. Through the use of absolute risk differences, changes in dyspnea and quality of life scores, minimum clinically important differences, and responder analyses, we’ll walk through how meta-analysis and individual trial outcomes data can be translated into usable presentations of the potential positive and negative impacts inhalers have on clinically relevant COPD outcomes. In addition we’ll address how this evidence can be used to clinically monitor patients once they are on inhalers for COPD. Appreciating the challenges of applying often relatively small clinical outcome changes that inhalers provide to decision making in a condition that has a relatively larger fluctuating and dynamic symptomatology, participants will consider the pros and cons of several approaches to the clinical monitoring of patients with COPD. <h3>Objectives</h3> Understand the importance of the clinical relevance of COPD outcomes in individual patient decisions. Integrate the use of clinical outcome data into translatable information for shared decision making. Appreciate and work within the challenges of how to apply clinical outcome data in a condition that is consistently fluctuating.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".