Effect of Head Position and Weight Loss on Intraocular Pressure in Obese Subjects
Bibliographic record
Abstract
PURPOSE: To measure the influence of body weight on postural intraocular pressure (IOP) changes and to determine if significant weight loss effects IOP. PATIENTS AND METHODS: In this prospective case-control study 25 morbidly obese subjects scheduled for bariatric surgery and 25 age-matched and sex-matched normal weight controls were recruited. Subjects had tonometry performed in 7 positions with the order randomized: sitting with the neck in neutral position, neck flexion at 30 degrees, extension at 30 degrees, supine, right, and left lateral decubitus, and with the head and upper body elevated at 30 degrees. The obese subjects were reassessed 1 to 2 years after bariatric surgery. RESULTS: Mean IOP in the obese group was significantly higher than the control group across all positions by a mean of 2.5±0.4 mm Hg (P<0.02). There was no significant difference in the magnitude of postural IOP change between obese and control subjects. In total, 19 obese subjects completed follow-up after bariatric surgery. Mean weight loss was 49.1±17.2 kg, 36% of total body weight. Mean IOP was significantly lower after bariatric surgery by 1.6±0.5 mm Hg (P<0.001). Linear regression demonstrated that every 10% body weight loss was correlated with 1.4 mm Hg decrease in IOP (r=-0.46). CONCLUSIONS: Obesity is associated with increased IOP compared with normal weight controls, but not with the magnitude of postural IOP change across different seated and supine positions. Significant weight loss after bariatric surgery is weakly associated with IOP lowering. The relationship between IOP, glaucoma, and obesity deserves further study.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".