Electronic Monitoring in an Acute Pain Management Service
Bibliographic record
Abstract
Objectives. This article will address the process involved in the development and implementation of a clinician-driven portable electronic chart on an Acute Pain Management Service (APMS). We describe the latest version of the program and provide 1 year of clinical data. Setting. Tertiary care center in Kingston, Ontario, Canada. Patients. All patients admitted to the APMS between August 1, 2005, and July 31, 2006. Results. A total of 8,726 APMS visits were made to 2,528 patients. Mean length of stay on the Service was 2.3 days. Sixty-one percent of patients reported an active pain score >3/10. Pain scores were highest with hip or knee surgery. Thirty-five percent of patients reported nausea. Conclusions. Executive sponsorship, alignment with institutional priorities, and user input are essential to the development, implementation, adoption, and sustainability of an electronic patient record. Ready access to data at the bedside can improve quality of care, while ongoing, comprehensive data can contribute to Phase IV drug trials. Incorporating both clinical and research outcomes in the database improves data quality and usability, but must be balanced with the impact of clinical time constraints on documentation. Wireless technology and Tablet computers provide portability and adequate screen size for documentation and reviewing of patient data on an acute pain service. It is necessary to provide solutions to process issues, such as printing electronic records during the transition from paper to electronic records.
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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".