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Enregistrement W2123000593 · doi:10.1016/j.jcrs.2004.06.066

Environmental factors and LASIK

2004· letter· en· W2123000593 sur OpenAlexaboutno aff
Louis E. Probst

Notice bibliographique

RevueJournal of Cataract & Refractive Surgery · 2004
Typeletter
Langueen
DomaineMedicine
ThématiqueOcular and Laser Science Research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLASIKKeratomileusisUnivariateLinear regressionVariablesConfoundingBayesian multivariate linear regressionMultivariate statisticsRegression analysisMultivariable calculusMultivariate analysisMedicineStatisticsMathematicsOphthalmologyCorneaEngineering

Résumé

récupéré en direct d'OpenAlex

The recent publication by Walter and Stevenson1 on the effect of environmental factors on laser in situ keratomileusis (LASIK) has received tremendous attention with reports throughout the media including Time magazine, the Chicago Tribune, the Los Angeles Times, and several television reports. These reports have caused concern in potential LASIK patients as they suggest that the results of LASIK depend on environmental factors such as outdoor humidity. This has been interpreted by some journalists to mean that LASIK is better performed in months with moderate humidity. While this paper has provided the best data to date demonstrating the association of LASIK results with procedure-room humidity,2 the statistical methods used have not been clearly reported and the results have not been clearly presented. As a result, the other associations described in the paper and reported in the media have been interpreted incorrectly. The 2 analyses used in this study are univariable and multivariable linear regression models (less correctly referred to in the paper as univariate and multivariate analyses). A univariable linear regression analysis determines the association between a single independent variable and a single dependent variable. In a study that has multiple independent variables, the results for 1 variable can be influenced by other “confounding” variables. In a study with multiple variables, multivariable linear regression analysis is used to determine the associations of independent variables while using various statistical methods to control for the other variables. As an example of the use of the 2 methods of linear regression, we can consider a study of the effect of 2 medications on blood pressure. To determine the effect of 2 drugs on blood pressure, regression analyses considering both age and the drug effects are performed. Univariable linear regression analysis would determine whether there was an association between the drugs and the blood pressure. However, the association could be explained by adjusting for the effect of patient age. A multivariable linear regression analysis would compare the 2 drugs, while making the statistical adjustment for age. In the paper by Walter and Stevenson, multivariable regression analysis is required to determine the significance of each multiple variable studied. Laser in situ keratomileusis enhancement rates were found to be significantly associated with age, procedure-room humidity, outdoor temperature, and 2-week preoperative outdoor humidity using univariable analysis. However, some of these variables could confound the others; ie, outdoor humidity could affect indoor humidity if there were not adequate indoor humidity control systems. In the multivariable model, the authors state that procedure-room humidity is associated with LASIK enhancement rates, but no mention is made of the associations of the other variables using multivariable analysis. Since the authors do not present the other results of the multivariable model, no comment can be made about the association of other factors with enhancement rates. Presumably the authors controlled for all other variables in the study and found them to be not significant. Once room humidity has been considered in the multivariable model, the effects of all other variables, including outdoor humidity, do not contribute significantly to explaining LASIK enhancement rates. For percentage of correction, the paper indicates that procedure-room humidity, outdoor temperature, 2-week preoperative mean outdoor humidity, and room temperature are associated with percentage of correction using the univariable linear regression analysis. In this case, the authors acknowledge that the multivariable model found that only room humidity was associated with percentage of correction, while outdoor temperature, procedure-room temperature, 2-week preoperative mean outdoor humidity, and room temperature did not have significant associations while controlling for the other confounding variables. The univariable association of these variables with percentage of correction disappears when other variables are “adjusted for” or “controlled for” with the multivariable analysis. Therefore, using multivariable linear regression analysis, this paper demonstrates that only procedure-room humidity is associated with LASIK enhancement rates and the percentage of correction. The authors suggest the development of a nomogram that considers indoor room humidity “since controlling indoor humidity is difficult.” While LASIK nomogram refinements are certainly 1 method to address the issue of humidity, the most obvious solution would be to control the indoor humidity. The Liebert Mini-Mate2® by the Liebert Corp. and CeilAiR® by Stultz Air Technology Systems provide excellent procedure humidity and temperature control with 2 degrees of variation of temperature and 5 units of variation of humidity through the year, even in the extreme temperature fluctuations of the Midwest. While these systems can cost between $20000 and $60000 to install, the expense would be justified considering the desire to further improve LASIK outcomes. Larry Stitt, MSc, Biostatistical Support Unit, Department of Epidemiology & Biostatistics, University of Western Ontario, London, Ontario, Canada, assisted with the statistical analysis. Louis Probst MD Chicago, Illinois, USA

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,008
Score d'incertitude au seuil0,028

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,003
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0010,000
Science ouverte0,0000,001
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0080,001

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 Opus0,027
Tête enseignante GPT0,286
Écart entre enseignants0,259 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations4
Publié2004
Routes d'admission1
Résumé présentoui

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