Improving Prevention, Early Recognition and Management of Acute Kidney Injury after Major Surgery: Results of a Planning Meeting with Multidisciplinary Stakeholders
Bibliographic record
Abstract
PURPOSE OF REVIEW: Acute kidney injury (AKI) is common after major surgery, and is associated with morbidity, mortality, increased length of hospital stay, and high health care costs. Although recent guidelines for AKI provide recommendations for identification of patients at risk, monitoring, diagnosis, and management of AKI, there is lack of understanding to guide successful implementation of these recommendations into clinical practice. SOURCES OF INFORMATION: We held a planning meeting with multidisciplinary stakeholders to identify barriers, facilitators, and strategies to implement recommendations for prevention, early identification, and management of AKI after major surgery. Barriers and facilitators to knowledge use for peri-operative AKI prevention and care were discussed. FINDINGS: Stakeholders identified barriers in knowledge (how to identify high-risk patients, what criteria to use for diagnosis of AKI), attitudes (self-efficacy in preventive care and management of AKI), and behaviors (common use of diuretics, non-steroidal anti-inflammatory drugs, withholding of intravenous fluids, and competing time demands in peri-operative care). Educational, informatics, and organizational interventions were identified by stakeholders as potentially useful elements for future interventions for peri-operative AKI. LIMITATION: Meeting participants were from a single centre. IMPLICATIONS: The information and recommendations obtained from this stakeholder's meeting will be useful to design interventions to improve prevention and early care for AKI after major surgery.
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 | 0.032 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".