Bibliographic record
Abstract
Key points Asthma patients want help to improve their ability to function in their daily lives (physical, social, occupational and emotional). Awareness of patients’ problems and a willingness to treat them may improve both asthma control and quality of life. The clinician and the patient should negotiate a treatment plan that addresses both asthma control and the patient's needs, and that the patient is willing to follow. Valid, easy-to-use, self-administered asthma quality-of-life questionnaires can be used in the clinic to identify quickly the patient's specific problems and treatment goals. Educational aims To define HRQL and emphasise its importance in patients with asthma. To describe the HRQL impairments experienced by adults and children with asthma. To discuss the role of HRQL in clinical practice and the concept of shared decision making. To provide information on the selection, methods of administration, analysis, interpretation and cultural adaptation of HRQL questionnaires Summary An important contributor to poor patient compliance with treatment instructions may be a discrepancy between the goals of the clinician and those of the patient. Improved clinician awareness of patients’ asthma-related quality-of-life goals and a readiness to address them may enhance patients’ willingness to take medications and, thus, improve both their asthma control and their quality of life. The aim of this article is to explain how quick, valid, easy-to-use, self-administered and clinic-friendly HRQL can be used to identify patients’ greatest needs, ascertain how troublesome they are and ensure that they are included in the treatment plan. HRQL questionnaires can also be used to monitor patient progress over time.
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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 | 0.042 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".