Résultats réfractifs et facteurs pronostiques de succès du traitement du kératocône par anneaux intracornéens : étude rétrospective sur 75 yeux
Bibliographic record
Abstract
PURPOSE: To define the prognostic factors for success and to evaluate the predictability of intracorneal ring segments (ICRS) in the treatment of keratoconus. METHODS: In this retrospective study conducted at the University Hospital of Nantes, Keraring ICRS were implanted in 75 eyes of 65 patients with keratoconus. Best spectacle corrected visual acuity (BSCVA), manifest refraction and corneal topography were analysed. To define prognostic factors, we compared the results of 2 groups: "IMP" (gain of at least 2 lines of BSCVA) and "ROS" (the others). We evaluated the predictability of the nomogram with a mathematical model proposed by Pena-Garcia et al. (IOVS 2012). RESULTS: At 3 months, BSCVA improved from 0.3 to 0.2 logMAR (P<0.05). A total of 61 % of the patients experienced a gain of at least 1 line of BSCVA. Spherical equivalent decreased by 2.32 diopters (D), cylinder decreased by 2.47 D, and maximal keratometry by 2.62 D (P<0.05 for each compared with preoperative values). A total of 90 % of the patients whose BSCVA did not improve achieved a significant refractive improvement. A preoperative BSCVA>0.3 logMAR is a prognostic factor for gain of at least 2 lines of BSCVA (P=1.6E-3). Predictability was fair: only 43 % had a±1D difference from the spherical equivalent predicted by the nomogram. There was no statistically significative difference between gain or loss of BSCVA predicted by the mathematical model and the postoperative results. CONCLUSIONS: ICRS are visually and refractively effective. Predictability could be improved by using mathematical models and knowledge of prognostic factors for success, allowing for better patient selection.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".