A Modified Delphi Process to Identify Process of Care Indicators for the Identification, Prevention and Management of Acute Kidney Injury after Major Surgery
Bibliographic record
Abstract
BACKGROUND: The outcomes of acute kidney injury (AKI) are well appreciated. However, valid indicators of high quality processes of care for AKI after major surgery are lacking. OBJECTIVES: To identify indicators of high quality processes of care related to AKI prevention, identification, and management after major surgery. DESIGN: A three stage modified Delphi process. SETTING: The study was conducted in Alberta, Canada using an online format. PARTICIPANTS: A panel of care providers from surgery, critical care, and nephrology. MEASUREMENTS: The degree of validity of candidate indicators were rated by panelists on a 7-point Likert scale that ranged from "strongly disagree" to "strongly agree". METHODS: A focused literature review was performed to identify candidate indicators. A modified Delphi process, with three rounds, was used to obtain expert consensus on the validity of potential process of care quality indicators. RESULTS: Thirty-three physicians participated (6 from surgery, 10 from critical care, and 17 from nephrology). A list of 58 potential process of care quality indicators for AKI after surgery was generated including 28 indicators from the initial literature review and 30 indicators suggested by panelists. Following the third round of questioning, 40 process of care indicators were identified with a high level of agreement for face validity; 16 of these reached high consensus among all panelists. LIMITATIONS: The consensus of panelists from Alberta, Canada may not be generalizable to other settings. The modified Delphi process did not focus on the feasibility of measuring these process indicators. CONCLUSIONS: These indicators can be used to measure and improve the quality of care for AKI after major surgery.
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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 | 0.193 | 0.169 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".