Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) statement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Spatial lifecourse epidemiology is an interdisciplinary field that utilizes advanced spatial, location-based, and artificial intelligence technologies to investigate the long-term effects of environmental, behavioural, psychosocial, and biological factors on health-related states and events and the underlying mechanisms. With the growing number of studies reporting findings from this field and the critical need for public health and policy decisions to be based on the strongest science possible, transparency and clarity in reporting in spatial lifecourse epidemiologic studies is essential. A task force supported by the International Initiative on Spatial Lifecourse Epidemiology (ISLE) identified a need for guidance in this area and developed a Spatial Lifecourse Epidemiology Reporting Standards (ISLE-ReSt) Statement. The aim is to provide a checklist of recommendations to improve and make more consistent reporting of spatial lifecourse epidemiologic studies. The STrengthening the Reporting of Observational Studies in Epidemiology (STROBE) Statement for cohort studies was identified as an appropriate starting point to provide initial items to consider for inclusion. Reporting standards for spatial data and methods were then integrated to form a single comprehensive checklist of reporting recommendations. The strength of our approach has been our international and multidisciplinary team of content experts and contributors who represent a wide range of relevant scientific conventions, and our adherence to international norms for the development of reporting guidelines. As spatial, location-based, and artificial intelligence technologies used in spatial lifecourse epidemiology continue to evolve at a rapid pace, it will be necessary to revisit and adapt the ISLE-ReSt at least every 2-3 years from its release.
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.
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 | 0.016 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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 it