Ophthalmology on Call: Evaluating the Volume, Urgency, and Type of Pages Received at a Tertiary Care Center
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: A significant proportion of on-call resident workload is related to answering and managing pages. Ophthalmology residents see high volumes of patients on call, but little is known about the profile of pages they receive. The objective of this study is to characterize the volume, type, and urgency of pages received by the ophthalmology on-call service. METHODS: A retrospective review of on-call pager log sheets and patient charts was performed at a single academic institution. Data were collected from July to December 2019, sampling the first seven days of each month. Data collected for each page included date/time of day, source, and primary concern. For each page leading to a patient encounter, time from page to patient assessment, patient demographics, and final diagnosis were recorded. Continuous variables were reported as mean values, whereas categorical variables were presented as percentages. A two-sample t-test and single-factor analysis of variance were employed. RESULTS: Over 42 days, 1108 pages were received. Over half of these calls required patient assessment, 71% of which were seen the same day. On average, 26 pages were received in 24 hours. Daytime weekday hours were significantly more busy than weekday nights or weekends (p<0.001). Patients and the emergency department each accounted for almost one-third of calls received. Retina- and cornea-related consults were most common. CONCLUSIONS: Pager volumes in ophthalmology are high and on-call patient volumes are rising. Answering pages increases the on-call resident's workload and has a negative impact on clinic flow. These data can be used to inform resident curriculum development, hospital system changes, patient education regarding appropriate paging, and medical school teaching.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.002 | 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 it