Protocol for development of SPIRIT and CONSORT extensions for reporting climate and environmental outcomes in randomised trials (SPIRIT-ICE and CONSORT-ICE)
Bibliographic record
Abstract
INTRODUCTION: The WHO has declared climate change the defining public health challenge of the 21st century. Incorporating climate and environmental outcomes in randomised trials is essential for enhancing healthcare treatments' sustainability and safeguarding global health. To implement such outcomes, it is necessary to establish a framework for unbiased and transparent planning and reporting. We aim to develop extensions to the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT 2025) and Consolidated Standards of Reporting Trials (CONSORT 2025) statements by introducing guidelines for reporting climate and environmental outcomes. METHODS AND ANALYSIS: This is a protocol for SPIRIT and CONSORT extensions on reporting climate and environmental outcomes in randomised trials termed SPIRIT-Implementing Climate and Environmental (ICE) and CONSORT-ICE. The development of the extensions will consist of five phases: phase 1-project launch, phase 2-review of the literature, phase 3-Delphi survey, phase 4-consensus meeting and phase 5-dissemination and implementation. The phases are expected to overlap. The SPIRIT-ICE and CONSORT-ICE extensions will be developed in parallel. The extensions will guide researchers on how and what to report when assessing climate and environmental outcomes. ETHICS AND DISSEMINATION: The protocol was submitted to the Danish Research Ethics Committees, Denmark in June 2025. Ethics approval is expected in September 2025. The SPIRIT and CONSORT extensions will be published in international peer-reviewed journals.
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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 | 0.251 | 0.514 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.174 | 0.054 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".