Patient reported outcome measures relevant to asthma remission: scoping review protocol
Bibliographic record
Abstract
Introduction: Asthma affects over 260 million people globally. Recent advances in asthma care have highlighted remission as a key treatment goal. While remission requires agreement between patients and healthcare providers, there is no standard way to assess the patient experience of remission. This scoping review aims to identify validated Patient Reported Outcome Measures (PROMs) that capture patient experiences of disease remission and may be applicable to asthma. Determining items and domains that are most important to patients will inform the development of a conceptual framework for a PROM for asthma remission. Methods and Analysis: This review will identify PROMs that quantify remission in long-term diseases. It will follow the Joanna Briggs Institute Manual and the PRISMA-ScR guidelines. Two independent reviewers will screen titles and abstracts following a training and calibration phase. Data extraction will also be performed independently by two authors, with disagreements resolved through discussion or a third reviewer. Ethics and Dissemination: No ethics approval is required as no human participants are involved. Findings will be shared at academic conferences and published in peer-reviewed journals.
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 | 0.097 | 0.108 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.092 | 0.015 |
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".