Defining the Outcome Measures for Out‐of‐hospital Trials in Acute Pulmonary Edema
Bibliographic record
Abstract
OBJECTIVE: Comparing studies of the effectiveness of out-of-hospital interventions in acute pulmonary edema (APE) is difficult due to the diversity of outcome measures used in the literature. The objective of this study was to define a set of clinically relevant outcome measures for future out-of-hospital trials in APE. METHODS: A Medline search and hand-search of bibliographies was undertaken to develop a list of APE outcome measures. A survey was mailed to a sample of 227 Canadian emergency physicians using the Dillman methodology, requesting that respondents select clinically relevant outcome measures from this list and rank them by importance. A selection frequency of >or=70% and a median ranking score were used to determine relevant outcome measures. RESULTS: The Medline and bibliography search identified 21 APE outcome measures. The survey response rate was 71%. Outcome measures selected most frequently were heart rate, respiratory rate, respiratory distress scale, subjective dyspnea scale, out-of-hospital intubation, emergency department (ED) intubation, survival to discharge, and out-of-hospital mortality. The median ranking score identified a similar set of measures: heart rate, respiratory rate, respiratory distress scale, subjective dyspnea scale, out-of-hospital intubation rate, and ED intubation rate. There was no significant difference in outcome selection between physicians who worked in communities with and without advanced out-of-hospital care. CONCLUSIONS: Clinically relevant out-of-hospital APE outcome measures were identified and endorsed by a representative survey of Canadian emergency physicians. Clinicians appear to favor short-term and non-mortality outcomes for out-of-hospital interventions. The use of this set of APE outcome measures may improve the design and comparability of future out-of-hospital trials.
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 | 0.221 | 0.491 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".