The carbon footprints of home and in‐center maintenance hemodialysis in the United Kingdom
Bibliographic record
Abstract
Climate change presents a global health threat. However, the provision of healthcare, including dialysis, is associated with greenhouse gas emissions. The aim of this study was to determine the carbon footprints of the differing modalities and treatment regimes used to deliver maintenance hemodialysis (HD), in order to inform carbon reduction strategies at the level of both individual treatments and HD programs. This was a component analysis study adhering to PAS2050. Emissions factors were applied to data that were collected for building energy use, travel and procurement. Thrice weekly in-center HD has a carbon footprint of 3.8 ton CO2 Eq per patient per year. The majority of emissions arise within the medical equipment (37%), energy use (21%), and patient travel (20%) sectors. The carbon footprint of providing home HD varies with the regime. For standard machines: 4 times weekly (4 days, 4.5 hours), 4.3 ton CO2 Eq; 5 times weekly (5 days, 4 hours), 5.1 ton CO2 Eq ; short daily (6 days, 2 hours), 5.2 ton CO2 Eq; nocturnal (3 nightly, 7 hours), 3.9 ton CO2 Eq; and nocturnal (6 nightly, 7 hours), 7.2 ton CO2 Eq. For NxStage equipment: short daily (5.5 days, 3 hours), 1.8 ton CO2 Eq; 6 nightly nocturnal (2.1 ton CO2 Eq). The carbon footprint of HD is influenced more by the frequency of treatments than by their duration. The anticipated rise in the prevalence of home HD patients, dialyzing more frequently and for longer than in-center patients, will increase the emissions associated with HD programs (despite reductions in patient travel emissions). Emerging technologies, such as NxStage, might offer a solution to this problem.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".