Degrowth in the clinical laboratory: A key step towards integrating planetary health into the healthcare system
Bibliographic record
Abstract
Healthcare contributes around 5-10% of global carbon emissions, alongside other pollutants, through utilization of over-stretched planetary resources. This creates a paradox in which efforts to protect health also generate risks to population health by contributing to the decline of planetary ecosystems- the foundation for health on which the healthcare system operates. This unsustainable cycle demands an urgent, unified front across all domains of clinical practice. Laboratory medicine, as a key entry point in the patient diagnostic pathway, is well-positioned to lead the required transformative change. While concepts such as sustainability and stewardship have been used interchangeably to rationalize resource use, the time has come to advance toward a model of 'degrowth' in the diagnostic laboratory. In healthcare, degrowth aims to minimize environmental harm by deliberately shrinking the consumption of unnecessary resources, particularly those from diagnostic and therapeutic interventions, without compromising patient outcomes. Diagnostic laboratories can support degrowth activities directly by adopting 'green laboratory' practices that include consuming less energy, minimizing waste (especially of reagents and non-recyclables) and optimizing test utilization by curbing low-value or unnecessary testing. They can also make an indirect impact by helping shift healthcare culture through shaping clinical guidelines using a degrowth lens, applying an environmental impact assessment whenever a new test is developed and advocating for sustainability declarations in publications that present new diagnostic approaches or technologies. When supported by effective stewardship programs, laboratories can serve as gatekeepers of diagnostic information and play a powerful role in aligning clinical decision-making with environmental responsibility. By embracing principles of degrowth in laboratory medicine, we have a chance, as well as a duty, to influence healthcare practices towards more ethical and environmentally responsible choices.
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 | 0.083 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.035 |
| Scholarly communication | 0.042 | 0.064 |
| Open science | 0.008 | 0.037 |
| Research integrity | 0.024 | 0.051 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".