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Enregistrement W2147802704 · doi:10.1046/j.1464-5491.2003.00992.x

Use of insulin glargine during embryogenesis in a pregnant woman with Type 1 diabetes

2003· letter· en· W2147802704 sur OpenAlexfundno aff
Andreas Holstein, A. Plaschke, E.‐H. Egberts

Notice bibliographique

RevueDiabetic Medicine · 2003
Typeletter
Langueen
DomaineMedicine
ThématiqueGestational Diabetes Research and Management
Établissements canadiensnon disponible
Organismes subventionnairesCanadian Institutes of Health ResearchMcGill UniversityChildren's Hospital FoundationHealth Sciences Centre Foundation
Mots-clésMedicine

Résumé

récupéré en direct d'OpenAlex

Technical failure in photographic screening for diabetic retinopathyDiabetic retinopathy is the largest single cause of registered blindness among people of working age.At any time, 10% of the diabetic population may have retinopathy requiring ophthalmological follow-up or treatment [1].The two main approaches to diabetic retinopathy screening include regular ophthalmoscopic examination and retinal photography with subsequent grading.Digital fundus photography has greatly replaced slide and polaroid photography and is a cost-effective system.However, good quality images are essential for accurate grading to be possible.The National Screening Committee (NSC) recommends that the technical failure rate for digital fundus photography should be less than 5% (www.diabeticretinopathy.screening.nhs.uk).In order to determine if this target is achievable, we have audited the technical failure rate at St James University Hospital.We completed the audit cycle and looked at 150 consecutive retinal images in May 2002, and another 150 images in September 2002.The images were taken by two photographers, both of whom had received an initial training of 3 weeks to familiarize them with ophthalmic photography, and had 1 year of practical experience before the start of the study.Based on the National Screening Committee criteria, the set of images from each patient were classified as being a technical success or a technical failure due to photographic error, or a technical failure due to patient factors such as media opacity, small pupil or patients with difficulty in positioning.The images were considered a technical failure due to photographic error if the correct number of images had not been taken, the images were not centred well, or had poor clarity obscuring view of 1/3 or more of the temporal image or the large temporal blood vessels.If the image quality was poor, the photographers were asked to provide a red reflex image to demonstrate media opacity, small pupil, etc.With such supporting evidence, the images were to be considered technical failure due to media opacity.In its absence, it was presumed that it was a technical failure due to photographic error.Our technical failure rate in this completed audit cycle was 7%.We could not achieve the recommended technical failure rate less than 5% as suggested by NSC.In all, there were 13 (4.3%)technical failures due to media opacity, small pupil or difficult positioning of the patient.This figure did not differ much between the audits, being six in the first audit and seven in the second.There were eight technical failures due to photographic error, five in the first audit and three in the second.It is possible that continued discussion with the photographers may reduce this figure further.This study suggests that the technical failure rate is greatly dependent on patient variable factors like cataract, small pupil, etc. and hence may be difficult to achieve.However, if we are able to reduce the technical failure rate due to photographic error to less than 1%, and assuming that the images failing due to patient factor remain constant at around 4%, the standard proposed by NSC would be achievable.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,635
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,029
Tête enseignante GPT0,260
Écart entre enseignants0,232 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations37
Publié2003
Routes d'admission1
Résumé présentoui

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