Blepharospasm in a Multiethnic Population (P3.348)
Bibliographic record
Abstract
Objective: To determine the incidence of dystonic blepharospasm and characterize antecedent diagnoses within a large, multiethnic integrated health maintenance organization. Background: There are limited data on blepharospasm incidence and prior reports have been based on a small number of cases. Design/methods: Incident blepharospasm cases were identified using electronic medical record review in >3 million members of Kaiser Permanente Northern California (KPNC) during 2003-2007. Final diagnosis was determined by consensus of two movement disorders specialists. Incidence rates were standardized using the 2000 U.S. Census population. Controls were matched for age, sex and membership duration (1 case: 10 controls). Odds ratios were determined using logistic regression adjusted for age, gender and membership duration. Results: Blepharospasm incidence standardized to the U.S. 2000 Census population was 1.45/100,000 person-years (95[percnt] CI, 0.53 to 2.37; women: 2.05, men: 0.83) based on 246 cases over 15.4 million person-years of risk. Incidence increased with age through the eighth decade and was highest in Caucasians (1.58) followed by Asians (1.32), African Americans (1.08) and Native Americans (0.95). Ocular diagnoses more common in patients with blepharospasm compared to controls before index date included were eye allergy (OR 2.82, 95[percnt] CI, 1.69 to 4.7), dry eyes (OR 9.19, 95[percnt] CI, 6.22 to 13.58), glaucoma (OR 2.76, 95[percnt] CI, 1.28 to 5.95), eye injury (OR 2.38, 95[percnt] CI, 1.44 to 3.94) and eye infection (OR 3.76, 95[percnt] CI, 2.58 to 5.47). Conclusions: Blepharospasm incidence is higher in women, in those of increasing age and of Caucasian race. Diagnoses preceding blepharospasm may reflect comorbid conditions, diagnostic errors or etiologic factors. Support: NIH-1R01-NS046340, AHRQ-R01-HS018413, Dystonia Medical Research Foundation, KPNC, James and Sharron Clark
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".