Measuring Quality-Weighted Hospital-Free Days in Acute Respiratory Failure: A Modified Delphi Study
Bibliographic record
Abstract
Abstract Rationale Hospital-free days (HFDs), a measure of the number of days alive spent outside the hospital, is increasingly used as an endpoint in studies of patients with acute respiratory failure (ARF) or other critical and serious illnesses. Current approaches to measuring HFDs do not account for decrements in functional status or quality of life that ARF survivors and family members value. Objectives To develop an acceptable approach to measure quality-weighted HFDs using patient-reported outcomes. Methods We conducted a four-round modified Delphi process among ARF experts: those with lived or professional experience. Experts rated survivorship domains, instrument and data collection characteristics, and methods to translate responses into quality-weighted HFDs. The consensus threshold was that ⩾70% of respondents rated an item “totally acceptable” or “acceptable” and ⩽15% of respondents rated the item “totally unacceptable,” “unacceptable,” or “slightly unacceptable.” Results Fifty-seven experts participated in round 1. Response rates were 82–93% for subsequent rounds. Priority survivorship domains were physical function and health-related quality of life. Participants reached a consensus that data collection during ARF recovery should take less than 15 minutes per assessment, allow surrogate completion when patients are unable, and continue for at least 24 months of follow-up. Using the EuroQol-5 Dimensions (EQ-5D) questionnaire to quality weight HFDs met consensus criteria for acceptability. A majority of panelists preferred quality-weighted HFDs to unweighted HFDs or survival for use in future ARF studies. Conclusions Quality-weighting HFDs using patient and/or surrogate responses to the EQ-5D captured stakeholder priorities and was acceptable to this Delphi panel.
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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 | 0.102 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".