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Record W3021752939 · doi:10.1002/ajim.23117

Types of ocular injury and their antecedent factors: A systematic review and meta‐analysis

2020· review· en· W3021752939 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicTraumatic Ocular and Foreign Body Injuries
Canadian institutionsCanadore CollegeLaurentian UniversityMcMaster UniversityUniversity of TorontoWestern University
Fundersnot available
KeywordsMedicineCINAHLPsycINFOSystematic reviewOccupational safety and healthCritical appraisalMEDLINEPoison controlMeta-analysisGrading (engineering)Occupational injuryInjury preventionOccupational medicinePopulationHuman factors and ergonomicsEnvironmental healthNursingPsychological interventionAlternative medicinePathology

Abstract

fetched live from OpenAlex

BACKGROUND: Ocular injuries are an important workplace hazard that can lead to vision loss, decreased functioning, and socioeconomic costs. The aim of this systematic review is to identify types of occupational ocular injuries and examine factors associated with these injuries. METHODS: Four health sciences databases (Ovid Medline, Embase, PsycINFO, and CINAHL) were reviewed to identify evidence pertaining to occupational ocular injuries. This systematic review was registered with PROSPERO (registration number: CRD42018089876) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The PICO (Population/Intervention/Comparison/Outcome) tool was used to support, structure, and improve our search strategy. RESULTS: Overall, 12 studies with quantitative Critical Appraisal Skills Programme grading scores were assessed in a systematic review and meta-analysis of ocular injuries in the workplace. The systematic review identified four main factors associated with occupational ocular injury: (a) use of eye protection at the time of the ocular injury, (b) being male, (c) exposure to biological or chemical occupational hazards, and (d) risk-taking behavior. CONCLUSIONS: Differences in risk between countries of origin, occupational sectors, and dates of publication, suggest likely differences or changes in safety procedures. We recommend that employers ensure that safety equipment is tailored to the protection of their specific occupational hazards, and that employees are adhering to safety protocols.

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 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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.361
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0200.002
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.095
GPT teacher head0.358
Teacher spread0.263 · 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