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Record W4282831893 · doi:10.1016/s2542-5196(22)00074-2

Addressing the environmental sustainability of eye health-care delivery: a scoping review

2022· review· en· W4282831893 on OpenAlexfundno aff
John Buchán, Cassandra L. Thiel, Annalien Steyn, John Somner, Rengaraj Venkatesh, Matthew J. Burton, Jacqueline Ramke

Bibliographic record

VenueThe Lancet Planetary Health · 2022
Typereview
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
FundersNational Institute for Health Research Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of OphthalmologyBritish Council for Prevention of BlindnessFred Hollows FoundationCBM InternationalQueen's UniversityMoorfields Eye CharityWellcome Trust
KeywordsSustainabilityEnvironmental planningHealth care deliveryMEDLINEHealth careEnvironmental resource managementBusinessMedicinePolitical scienceGeographyEnvironmental scienceEcology

Abstract

fetched live from OpenAlex

The demand for eye care-the most common medical speciality in some countries-is increasing globally due to both demographic change and the development of eye health-care services in low-income and middle-income countries. This expansion of service provision needs to be environmentally sustainable. We conducted a scoping review to establish the nature and extent of the literature describing the environmental costs of delivering eye-care services, identify interventions to diminish the environmental impact of eye care, and identify key sustainability themes that are not yet being addressed. We identified 16 peer-reviewed articles for analysis, all published since 2009. Despite a paucity of research evidence, there is a need for the measurement of environmental impacts associated with eye care to be standardised along with the methodological tools to assess these impacts. The vastly different environmental costs of delivering clinical services with similar clinical outcomes in different regulatory settings is striking; in one example, a phacoemulsification cataract extraction in a UK hospital produced more than 20 times the greenhouse gas emission of the same procedure in an Indian hospital. The environmental costs must be systematically included when evaluating the risks and benefits of new interventions or policies aimed at promoting safety in high-income countries.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.876
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.273
GPT teacher head0.441
Teacher spread0.168 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations54
Published2022
Admission routes1
Has abstractyes

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