Ultrasound biomicroscopy of pseudophakic eyes with chronic postoperative inflammation
Bibliographic record
Abstract
PURPOSE: To evaluate the ultrasound biomicroscopy (UBM) findings in pseudophakic eyes with chronic noninfectious postoperative inflammation and discuss the use of the technique in these cases. SETTING: Uveitis Service, Department of Ophthalmology, McGill University, Montréal, Québec, Canada. METHODS: Fifty-four eyes of 51 patients with chronic noninfectious postoperative inflammation were prospectively evaluated between January 1998 and September 2001. Patients with aphakia, a dislocated intraocular lens (IOL) in the posterior segment, and endophthalmitis were excluded. All patients had a UBM examination that comprised locating the IOL position, investigating the presence of lens remnants, and evaluating the anterior segment of the eye. RESULTS: Ultrasound biomicroscopic examination revealed IOL misplacement in 37 eyes (68.5%). Of these, 23 (62.2%) had a sulcus-implanted posterior chamber IOL (PC IOL), 9 (24.3%) an in-the-bag PC IOL, and 5 (13.5%) an anterior chamber IOL. Haptic misplacement was significantly higher with sulcus-implanted PC IOLs than with in-the-bag PC IOLs (P<.01). Other UBM findings included edematous ciliary body processes and hypoechogenic and/or thickened ciliary bodies in 11 eyes (20.4%), peripheral anterior synechias in 8 eyes (14.8%), a significant number of lens remnants (graded as severe) in 6 eyes (11.1%), a thick cyclitic membrane in 3 eyes (5.6%), and an early cyclitic membrane in 2 eyes (3.7%). CONCLUSIONS: Irritation of ocular tissues by an IOL was the main cause of chronic postoperative noninfectious inflammation in pseudophakic eyes. Therefore, detecting the IOL position and its relationships to ocular tissues is very important in planning the treatment. Ultrasound biomicroscopy is a practical method that accurately provides this information.
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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) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".