Signing on to sign out, part 2: describing the success of a web-based patient sign-out application and how it will serve as a platform for an electronic discharge summary program.
Bibliographic record
Abstract
Sunnybrook Health Sciences Centre developed and implemented a physician-focused web-based patient sign-out application in January of 2005 with 40 different groups throughout the hospital now using it. More groups are continuing to request access to the system, including nursing and other multidisciplinary groups. The success of the system is attributable to its simplicity and usability as there is rarely any downtime and no formal training for physicians is ever necessary. The next step is to create an electronic discharge summary program. Using an electronic system to complete discharge summaries will allow more efficient completion of discharge summaries, improve the quality of discharge summaries and improve the timeliness of delivery to the family physician. Integrating the electronic discharge summary program with the current sign-out application is a logical approach because the two processes follow each other in the flow of care, information from the sign-out application is transferable to the discharge summary and both processes are essential for maintaining the continuity of care.
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 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 | 0.002 | 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) | 0.000 | 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 teacher head, 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".