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Record W4411771565 · doi:10.70070/rxw4w903

How do Different Surgical Techniques for Cataract Removal Impact Quality of Life And Functional Vision in Elderly Patients? : A Systematic Review

2025· review· en· W4411771565 on OpenAlexaff
Roysam Azmal sitanggang, Sita Pradjnadewi

Bibliographic record

VenueThe Indonesian Journal of General Medicine · 2025
Typereview
Languageen
FieldMedicine
TopicRetinal Imaging and Analysis
Canadian institutionsKelowna General Hospital
Fundersnot available
KeywordsQuality of life (healthcare)MedicineOptometryQuality (philosophy)Nursing

Abstract

fetched live from OpenAlex

Introduction: Age-related cataracts significantly impair vision and diminish the quality of life in elderly individuals worldwide. Cataract surgery is the primary intervention to restore vision and improve daily functioning, but the variety of surgical techniques and intraocular lens (IOL) options necessitates understanding their impact on patient outcomes. This systematic review evaluates the impact of different cataract surgery techniques on the quality of life and functional vision of elderly patients, synthesizing evidence from recent clinical trials and observational studies to identify optimal surgical approaches. Methods: This systematic review adhered to the PRISMA 2020 guidelines. We included studies focusing on patients aged 60 and older with age-related cataracts, evaluating surgical techniques like phacoemulsification, MSICS, or ECCE. Eligible studies were primary research (RCT, cohort, case-control) or systematic reviews/meta-analyses, measuring both quality of life and functional vision outcomes with at least a 3-month follow-up. Data extraction by a large language model focused on study design, setting, participant demographics, surgical technique, and visual performance/quality of life outcomes. The search strategy utilized Boolean MeSH keywords across databases like PubMed, Semantic Scholar, Springer, and Google Scholar. Results: Out of 1538 initial records, 14 studies were included. Visual acuity improved in 13 out of 14 studies, with 10 showing statistically significant improvement. All 14 studies reported improved functional vision or patient-reported outcomes, with 10 achieving statistical significance. Six studies found significant quality of life improvement after intervention. Comparisons revealed no significant difference between femtosecond laser-assisted and standard phacoemulsification, while multifocal/trifocal IOLs improved intermediate/near vision but increased dysphotopsia. Second eye surgery showed additional gains. MSICS was found to be cost-effective with comparable outcomes to phacoemulsification in resource-limited settings. Active-fluidics systems may offer faster early recovery. Discussion: The studies consistently show significant improvements in visual function and quality of life across various cataract surgery techniques, including phacoemulsification and MSICS. Second eye surgery further amplifies these benefits, encompassing socioemotional well-being. While advanced technologies like femtosecond laser-assisted phacoemulsification offer precision, they may not yield significantly better clinical or quality of life results compared to standard methods. The choice of IOL significantly impacts vision, with multifocal and trifocal lenses improving intermediate and near vision but potentially increasing dysphotopsia. MSICS provides a cost-effective alternative in certain settings , and active-fluidics systems may enhance early recovery. Patient-reported outcomes are crucial for capturing the real-world impact of surgery. Conclusion: Cataract surgery is highly effective in restoring vision and enhancing quality of life for elderly patients, with consistent improvements across various techniques. Phacoemulsification remains widely adopted, and advanced IOLs expand visual optimization. Bilateral surgery maximizes functional and emotional gains. Cost-effectiveness of MSICS and the promise of emerging technologies like active-fluidics systems underscore the need for individualized care based on patient needs, cost, and potential side effects.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.160
Threshold uncertainty score0.719

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.042
GPT teacher head0.390
Teacher spread0.348 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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