Anomalies in the Prescribing of Soft Contact Lens Power
Notice bibliographique
Résumé
OBJECTIVES: To determine the proportion of prescribed soft lenses rounded to the nearest half diopter and any variations from country to country and between lens types. METHODS: Marketing data were obtained for soft lenses supplied during a 1-year period for lenses representing each of the following categories: mid-water hydrogel (MWH), silicone hydrogel, daily disposable, and toric silicone hydrogel (TSH). The data were analyzed for several countries/regions. Spherical lenses were analyzed in the range 1.00 to 5.75 D for plus and minus powers, and toric lenses in the range 0.50 to 5.75 D. This ensured a similar number of lenses in full or half diopter powers were compared with quarter and three-quarter diopter powers, and that there was no enforced rounding due to nonavailability of powers. By comparing the proportion of lenses from the 2 power groups, the proportion of lenses rounded to the nearest half diopter was estimated. It was assumed that half the difference between the totals of the 2 power groups represented those lenses dispensed to the nearest half diopter and, therefore, dispensed inaccurately; this was termed the "rounding rate" (RR). RESULTS: The power distribution curve for the sphere powers spiked in half diopter steps, illustrated a bias toward prescribing full and half diopter powers. With all lenses, the RR varied widely between countries. For the MWH, this ranged from 1.7% (Canada) to 11.6% (Iberia). The RRs were 2 to 3 times higher for plus than minus power lenses, however, this also varied by country. Overall, the RRs were lower for the silicone hydrogel and daily disposable contact lenses compared with the MWH, in particular for France and Iberia. The TSH results showed the greatest consistency between countries, with RRs ranging from 3.9% (Germany) to 9.5% (Rest of Europe). Most countries showed similar or lower RRs for TSH compared with MWH although, for some countries (e.g., United Kingdom, Nordic), these were higher. There was less difference in RRs for TSH lenses between plus and minus spheres. CONCLUSION: A surprising proportion of soft lenses are prescribed to the nearest half diopter, although this varies according to lens type. There are also considerable variations between countries, presumably due to differences in training, fitting practices, and supply routes. These findings suggest that there is widespread room for improvement in the prescribing accuracy of soft contact lenses.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,003 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».