MétaCan
Menu
Back to cohort

Is the built environment associated with morbidity and mortality? A systematic review of evidence from Germany

2018· review· en· W4394559026 on OpenAlexaboutno aff
Maike Schulz, Matthias Romppel, Gesine Grande

Bibliographic record

VenueFigshare · 2018
Typereview
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyEnvironmental science

Abstract

fetched live from OpenAlex

The empirical evidence on this relationship mainly comes from Anglo-American countries whereas evidence from Germany is only emerging. Our objective is to provide a narrative overview and critical appraisal of the existing empirical evidence on the relationship between the built environment and morbidity/mortality in Germany. We conducted a systematic literature search where we included all empirical studies that linked the built environment aspects with morbidity or mortality outcomes. Findings were summarized and critically evaluated according to the Newcastle Ottawa Scale. Eighteen studies met the inclusion criteria and underwent in-depth analysis. Findings indicate that traffic exposure and green space tend to be associated with acute respiratory symptoms but not with chronic respiratory conditions. Evidence was inconsistent for the role of infrastructural aspects and urbanicity. Our review confirms the well-established association between traffic and respiratory health. Yet, the consistency between self-reported and objective measures of respiratory health should be investigated in more detail.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.010
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.028
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0090.008
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.396
GPT teacher head0.476
Teacher spread0.080 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations0
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueFigshareSame topicNoise Effects and ManagementFrench-language works237,207