Quality of Life After Cataract Surgery in Jordan
Bibliographic record
Abstract
PURPOSE: Cataract surgery is used for the removal of cloudy lens of eyes to reduce chances of blindness or any kind of visual impairments. The surgery is helpful to treat vision impairment, reduce chances of blindness, and bring positive impacts on quality of life among patients. The study aims to demonstrate key improvements in quality of life in terms of visual acuity and general quality of life after cataract surgery in Amman, Jordan.METHODS: The effect of cataract surgery on quality of life is assessed by using qualitative research methodology based on interviews of patients that have undergone cataract surgery. After collection of data, it is organized in forms of themes, and thematic analysis method was used for analysis of these themes.RESULTS: The outcomes obtained from data collected from participants have provided evidences that quality of life of participants has been improved after this surgery. Moreover, this study has also revealed that cataract surgery is effective to improve quality of life of participants by providing psychological, social, and emotional support.CONCLUSION: The study has concluded that cataract surgery in Jordan not only improve quality of life in terms of visual acuity, but also bring improvements in psychological and mental wellness of a person.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".