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Record W2944273027 · doi:10.15353/cjo.78.467

Mes tatouages ont causé ma sécheresse oculaire?

2016· article· fr· W2944273027 on OpenAlexvenueno aff
Caitlin J Morrison, Joseph M Stamm

Bibliographic record

VenueCanadian journal of optometry/CJO. Canadian journal of optometry · 2016
Typearticle
Languagefr
FieldMedicine
TopicOcular Surface and Contact Lens
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineGynecology

Abstract

fetched live from OpenAlex

But. Le présent rapport de cas décrit les répercussions potentielles d’un tatouage de contour des yeux sur la structure et le fonctionnement des paupières, soit des symptômes accrus de sécheresse oculaire et d’autres anomalies. Rapport de cas. Une femme de 59 ans d’origine hispanique, aux prises depuis longtemps avec des symptômes de sécheresse oculaire peu soulagés par des larmes artificielles, a consulté pour une évaluation. Les examens d’imagerie ont révélé une perte de glandes de Meibomius, probablement causée par le tatouage de contour des yeux. L’application de compresses chaudes, le massage palpébral et l’utilisation de larmes artificielles à base de lipides ont permis d’atténuer les symptômes et d’améliorer les mesures objectives. Conclusions. Un contour des yeux tatoué de façon permanente peut accroître la sécheresse oculaire par deux mécanismes principaux : perturbation de l’architecture des paupières et inflammation chronique due aux granules de pigments dans l’encre de tatouage. Reconnaître ces possibles effets chez les patientes ayant le contour des yeux tatoué peut aider à offrir un traitement adapté à l’étiologie de la sécheresse oculaire : stimuler les glandes de Meibomius restantes par des compresses chaudes et un massage palpébral, et utiliser des larmes artificielles à base de lipides pour pallier l’absence de sécrétion de lipides par les glandes de Meibomius qui manquent.

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)0.003
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 score0.011
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.002

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 head0.013
GPT teacher head0.298
Teacher spread0.285 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

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