Is home peak expiratory flow monitoring effective for controlling asthma symptoms?
Bibliographic record
Abstract
A case in which a home peak expiratory flow (PEF) monitoring device was recommended led us to review the evidence examining this intervention. The clinical question to be answered was: should these devices be consistently recommended to all patients with asthma? A comprehensive search revealed eight randomized controlled trials, one review and one consensus report. Four trials provided all subjects with asthma education and compared patient-specific action plans based on symptoms to those based on PEF readings. Four trials compared usual asthma care to peak flow monitoring (PFM) and varied in both their content and intensity of asthma education. Six out of eight studies showed improvement in some selected markers of asthma morbidity with home PFM-based action plans. Improvements were also observed in patients using a symptom-based action plan. These studies did not demonstrate any obvious advantage of PFM compared with symptom-based monitoring but did suggest that a monitoring plan with predetermined actions based on PEF measurements or symptoms can lead to improved asthma control. Although not specifically studied, PFM may be more appropriate and effective for patients who have difficulty identifying worsening of asthma control through symptom monitoring.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".