Assessing the Current Evidence on Environmental Sustainability in Nephrology: Protocol for a Scoping Review by the International Society of Nephrology, Emerging Leaders Program Cohort 2022-2024
Bibliographic record
Abstract
Background: Human-induced climate change is a significant threat to global public health. The healthcare sector contributes significantly to environmental damage through resource depletion and greenhouse gas emissions. Furthermore, nephrology practices have a disproportionately higher share in the carbon footprint produced by medical therapies (e.g. dialysis). This review aims to map the available evidence of green/sustainable nephrology in the literature to better understand the current lacunae in the evidence and challenges faced while adopting eco-friendly practices. Methods: A search strategy, developed in collaboration with a medical librarian to be used in Medline, and adapted for other databases (PubMed, Embase, Cochrane Library, CINAHL), will be used to retrieve references. A secondary manual search of all references from included studies will be undertaken (snowballing approach). All publications (including original studies, case reports, editorials, review articles, editorial letters, positional statements from professional societies, and conference abstracts) addressing any environmental impact of activity in kidney care; current knowledge or awareness of environmental impact of kidney care impact; any activity, strategy or effort focused on environmental sustainability of kidney care activity; or any barrier or challenge faced in adopting environmentally sustainable kidney care activity will be included. A data extraction table will be used to record the key components and information from the retrieved papers. Extracted data will be analyzed qualitatively using preidentified and newly identified themes. Results: Results will be summarized using descriptive statistics and narrative summaries around the identified themes, to explore the environmental impact of kidney care and the application of sustainability approaches. Conclusions: Results from this scoping review, which should be available for presentation in the fall of 2023, will help facilitate the formulation of a planned toolkit by our group, based on relevant components identified, which can direct healthcare professionals to vital resources in sustainable nephrology.
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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 | 0.133 | 0.124 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.019 |
| Bibliometrics | 0.028 | 0.024 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.066 | 0.013 |
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".