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Record W4404972634 · doi:10.7759/cureus.75011

Organic Foreign Body in the Eye, a Diagnostic Challenge: A Case of a Wooden, Intra-orbital Foreign Body Presenting at the Emergency Department

2024· article· en· W4404972634 on OpenAlexaff
Aaruran Nadarajasundaram

Bibliographic record

VenueCureus · 2024
Typearticle
Languageen
FieldMedicine
TopicTraumatic Ocular and Foreign Body Injuries
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsMedicineForeign bodyEmergency departmentOrbit (dynamics)EyelidIntravenous antibioticsForeign BodiesSurgeryMedical historyGeneral surgeryAntibioticsNursing

Abstract

fetched live from OpenAlex

Intra-orbital organic foreign body injuries occur within the eye but without the involvement of the orbit itself. A 39-year-old man self-presented to the emergency department complaining of sudden onset of pain surrounding his left eye and of reduced vision. The initial examination was unremarkable except for two healing lesion marks above his left upper eyelid. Some swelling with erythematous skin changes was also noted. Computer tomography did not identify a conclusive cause. The patient was assessed by an Ophthalmologist in Eye Casualty and commenced intravenous antibiotics for infection secondary to an intra-orbital wooden foreign body. The patient underwent surgery to remove the foreign body, experienced no postoperative complications, and was discharged following a brief medical admission for intravenous antibiotic administration, with vision returning to normal in the affected eye. This case report showcases the difficulty of such cases, given the organic nature of the foreign body. It also highlights the need for high clinical suspicion with thorough history-taking, as well as physician collaboration, to ensure organic foreign bodies are considered in similar presentations or cases.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.002
Scholarly communication0.0020.003
Open science0.0010.002
Research integrity0.0060.003
Insufficient payload (model declined to judge)0.0020.001

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 head0.019
GPT teacher head0.302
Teacher spread0.284 · 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 designCase report
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

Citations1
Published2024
Admission routes1
Has abstractyes

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