MétaCan
Menu
Back to cohort
Record W7036618304

Characteristics, Etiological Factors, and Visual Outcomes of Pediatric Open Globe Injuries in Central Saudi Arabia: A 22-Year Retrospective Study

2023· article· en· W7036618304 on OpenAlexaboutno aff

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2023
Typearticle
Languageen
FieldComputer Science
TopicMachine Learning in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsEtiologyRetrospective cohort studyVisual acuityPresentation (obstetrics)Red eyeGlobe
DOInot available

Abstract

fetched live from OpenAlex

Huda Al Ghadeer,1 Rajiv Khandekar2,3 1Emergency Department, King Khaled Eye Specialist Hospital, Riyadh, Saudi Arabia; 2Research Department, King Khaled Eye Specialist Hospital, Riyadh, Saudi Arabia; 3Department of Ophthalmology and Vision Sciences, Faculty of Medicine, University of British Columbia, Vancouver, BC, CanadaCorrespondence: Huda Al Ghadeer, Emergency Department, King Khaled Eye Specialist Hospital, PO Box 7191, Riyadh, 11462, Saudi Arabia, Tel +966 1 4821234 ext. 2500, Email hghadeer@kkesh.med.saPurpose: To discuss the characteristics, etiological factors, and visual outcomes of open globe injuries (OGIs) in children at a tertiary eye hospital in Riyadh, Central Saudi Arabia.Methods: This was a hospital-based cohort study conducted in 2021. Children aged ≤ 16 years with OGI based on the Birmingham Eye Trauma Terminology classification were included. The age, gender, type, cause of OGI, and vision were recorded, as well as uncorrected and best corrected visual acuity (UCVA and BCVA, respectively). There was a change in the UCVA and BCVA one year after management. BCVA following management was linked to a variety of factors.Results: There were 664 eyes with OGI. [median age 5.1, 461 (69.6%) boys]. UCVA at presentation was < 20/400 in 525 (79%) of eyes with OGI. Injuries were mainly due to metallic objects in 195 (29.4%), glass in 102 (15.4%), and fireworks in 62 (9.4%). The Change in visual impairment grade in UCVA and BCVA after management compared to the initial presentation was significant (p < 0.001). Improvement of two lines of BCVA was noted in 345 (52%), no change in (

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

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

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 head0.169
GPT teacher head0.535
Teacher spread0.366 · 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 designObservational
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueDOAJ (DOAJ: Directory of Open Access Journals)Same topicMachine Learning in HealthcareFrench-language works237,207