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Comment on: Surgeon adoption of immediate sequential bilateral cataract surgery in the United States from 2018 to 2022

2025· article· en· W4412597683 on OpenAlexaboutno aff
Bharat Gurnani, Kirandeep Kaur

Bibliographic record

VenueJournal of Cataract & Refractive Surgery · 2025
Typearticle
Languageen
FieldMedicine
TopicIntraocular Surgery and Lenses
Canadian institutionsnot available
Fundersnot available
KeywordsCataract surgeryMedicineGeneral surgeryOphthalmologyOptometrySurgery

Abstract

fetched live from OpenAlex

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?

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)0.007
metaresearch head score (Gemma)0.066
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.029
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.066
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0020.004
Open science0.0050.002
Research integrity0.0290.024
Insufficient payload (model declined to judge)0.0120.008

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 head0.027
GPT teacher head0.299
Teacher spread0.272 · 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 designNot applicable
Domainnot available
GenreCommentary

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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