Ophthalmology on Call: Evaluating the Volume, Urgency, and Type of Pages Received at a Tertiary Care Center
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".