Factors Associated With Adherence to Outpatient Follow-Up in Patients With Idiopathic Intracranial Hypertension (IIH)
Bibliographic record
Abstract
Idiopathic intracranial hypertension (IIH) is a chronic condition characterized by raised intracranial pressure of undetectable origin, that causes morbidity due to debilitating headaches and vision loss. Continuity of outpatient care is important to monitor for permanent vision loss, manage symptoms and limit emergency care. The purpose of this retrospective study was to identify factors associated with neuro-ophthalmology follow-up appointment completion among patients with IIH at a US academic medical center in order to establish evidence-based interventions to improve adherence patterns. Included are 111 completed or no-show neuro-ophthalmology return outpatient appointments by 23 subjects with IIH. Generalized estimating equation models were used to assess association between appointment completion status and factors previously shown to be associated with appointment adherence. Appointments were more likely to be completed during the summer (p=0.08) and by subjects with headache symptoms (p=0.06), however none of the patient factors reached statistical significance. Completed and no-show appointments did not differ by subject demographic or insurance factors. Further studies are needed to identify risk factors for lack of appointment adherence by patients with IIH, particularly those amenable to intervention, in order to improve continuity of care for IIH.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".