Compliance Barriers in Glaucoma: A Systematic Classification
Bibliographic record
Abstract
PURPOSE: To systematically identify and describe common obstacles to medication adherence (i.e., compliance) for patients with glaucoma. METHODS: A prospective case series of structured interviews were conducted with 48 patients with glaucoma. The subjects' responses were recorded verbatim on interview forms as well as recorded on audiotapes. Situational obstacles to medication adherence were elicited. Using hierarchical cluster analysis, the situational descriptions were stratified, grouped, and analyzed by frequency distribution. RESULTS: Seventy-one unique situational obstacles were reported. These were then grouped into 4 defined and separate categories: situational/environmental factors (35 of 71 situations; 49%), medication regimen (23 of 71; 32%), patient factors (11 of 71; 16%), and provider factors (2 of 71; 3%). CONCLUSION: Significant barriers to compliance exist for patients with glaucoma in addition to those cited by previous ophthalmic studies. A systematic classification (i.e., taxonomy) of these barriers was formulated to assist in optimizing patient education and problem-solving regarding prescribed therapeutic regimens.
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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 | 0.015 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".