NIKE: a new clinical tool for establishing levels of indications for cataract surgery
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to construct a new clinical tool for establishing levels of indications for cataract surgery, and to validate this tool. METHODS: Teams from nine eye clinics reached an agreement about the need to develop a clinical tool for setting levels of indications for cataract surgery and about the items that should be included in the tool. The tool was to be called 'NIKE' (Nationell Indikationsmodell för Kataraktextraktion). The Canadian Cataract Priority Criteria Tool served as a model for the NIKE tool, which was modified for Swedish conditions. Items included in the tool were visual acuity of both eyes, patients' perceived difficulties in day-to-day life, cataract symptoms, the ability to live independently, and medical/ophthalmic reasons for surgery. The tool was validated and tested in 343 cataract surgery patients. Validity, stability and reliability were tested and the outcome of surgery was studied in relation to the indication setting. RESULTS: Four indication groups (IGs) were suggested. The group with the greatest indications for surgery was named group 1 and that with the lowest, group 4. Validity was proved to be good. Surgery had the greatest impact on the group with the highest indications for surgery. Test-retest reliability test and interexaminer tests of indication settings showed statistically significant intraclass correlations (intraclass correlation coefficients [ICCs] 0.526 and 0.923, respectively). CONCLUSIONS: A new clinical tool for indication setting in cataract surgery is presented. This tool, the NIKE, takes into account both visual acuity and the patient's perceived problems in day-to-day life because of cataract. The tool seems to be stable and reliable and neutral towards different examiners.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".