Comment on: Surgeon adoption of immediate sequential bilateral cataract surgery in the United States from 2018 to 2022
Notice bibliographique
Résumé
We read with great interest the study by Ali et al. on the trends and surgeon characteristics associated with immediate sequential bilateral cataract surgery (ISBCS) adoption in the United States.1 The authors provide a valuable insight into the increasing but still limited acceptance of ISBCS among ophthalmic surgeons. However, we have several concerns and suggestions regarding the study's methodology and interpretation that warrant further discussion. First, the authors describe their study as a cross-sectional analysis using Medicare claims data from 2018 to 2022, but it is unclear whether it includes a temporal trend analysis or is merely descriptive. Although the Cochran-Armitage trend test is used, did the authors consider using more robust time-series modeling or longitudinal regression analysis to better characterize ISBCS adoption patterns?2 Second, the study excludes patients younger than 65 years based on Medicare eligibility, which may underestimate the true ISBCS rate in private insurance settings or younger patients with cataracts. Did the authors consider validating their findings with data from private insurance databases or large-scale ophthalmology registries? Third, one of the critical concerns with ISBCS adoption is appropriate patient selection. Although demographic and clinical differences between ISBCS and delayed cohorts are described, were factors such as age, comorbidities, or socioeconomic status driving patient selection, and could propensity-score matching reduce confounding? Fourth, the study finds that surgeons in the Western United States are twice as likely to perform ISBCS compared with those in the South. Although the authors suggest this may be due to the prevalence of Kaiser Permanente hospitals in the West, no institutional-level data are provided to confirm this. Did the authors assess regional reimbursement policies or state-level regulations on ISBCS? Fifth, the racial disparities in ISBCS adoption raise important questions. The data suggest Black and Native American patients had lower ISBCS rates, but the study does not explore whether provider bias, insurance differences, or patient preferences influenced this disparity. The study identifies younger, high-volume surgeons as more likely to adopt ISBCS. However, it would be useful to know: Were these surgeons in academic institutions or private practice? Sixth, did hospital policies or malpractice concerns deter older surgeons from ISBCS adoption? How did institutional policies, medico-legal concerns, patient counseling practices, and the 50% reduction in second-eye reimbursement influence surgeon uptake, and how do these factors compare with international models (eg, Sweden, Canada, and the United Kingdom)? Finally, one major limitation of using Medicare claims data is the lack of clinical outcomes such as visual acuity, complications (eg, endophthalmitis and macular edema), or patient-reported satisfaction. Did the authors attempt to link their dataset to clinical registries (eg, IRIS Registry) to assess whether ISBCS outcomes matched delayed sequential bilateral cataract surgery outcomes? Could the rate of postoperative visits, unplanned reoperations, or refractive adjustments provide insights into ISBCS safety?
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 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 | 0,004 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».