A-204 Commitment to Sustainability From Laboratory Medicine
Bibliographic record
Abstract
Abstract Background According to McGill University, sustainability means “meeting our needs without compromising the ability of future generations to meet their own needs”. Sustainable environmental practices improve water and air quality, reduce landfills, and increase renewable energy sources in the long term. In response to the growing urgency of global sustainability challenges, the healthcare industry is taking a step forward to reduce its environmental impact. University of Malaya Medical Centre (UMMC) is a government-funded teaching hospital and medical institution, which processes about 3.95 million chemistry and 371,000 immunoassay tests per year. This study aims to evaluate the sustainability aspects of Atellica Solution, focusing on water usage, power consumption, carbon dioxide emissions and plastic waste generations. Methods Power and water consumption for the analysers Advia Chemistry 2400 & XPT and Advia Centaur XPT were obtained from Atellica Solution using the year 2022 test volumes and compared with those of Siemens legacy system for the year 2019 test volumes. In addition, the reduction of plastic usage was also assessed for the year 2019 and 2022. Results With innovative Atellica Solution replacing Siemens legacy system, the data showed reduction in water and energy consumption by 36.44% and 47.74% respectively, whereas the annual throughput increased by 13.7%. By using Atellica INTELIQ QC management, fewer aliquot tubes were used and thus plastic waste was reduced by 168 kg. These reductions combined to lower CO2 emissions by about 16 tons. Conclusions Atellica Solution is designed for high throughput and sustainability, reducing water and energy consumption while also significantly reducing CO2 emissions. Furthermore, it enables faster, streamlined quality control management with Atellica INTELIQ QC management system. UMMC is strongly committed to minimizing laboratories’ environmental footprint and provide better access to care eventually contributing to the well-being of patients.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.040 | 0.011 |
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".