P056 Improving PONS (post-operative neurological symptoms) follow-up: a pathway & e-charting approach
Bibliographic record
Abstract
Please confirm that an ethics committee approval has been applied for or granted: Not relevant (see information at the bottom of this page) Background and Aims Peripheral nerve blocks provide anesthesia and pain management benefits but carry approximately a 3% risk of post-operative neurological symptoms (PONS). The risk of long term injury is 2-4 per 10000. Factors which contribute to PONS include surgical, anesthetic and patient factors like positioning, tourniquet ischemia, pre-existing deficits, diabetes and receiving a nerve block. Identifying these risk factors for PONS is crucial, but our institution was limited by inconsistent reporting due to a lack of a standardized referral system. We therefore undertook a Quality Improvement Project (QIP) to address this gap in our practice. We aimed to develop a system to capture, track and manage PONS cases after peripheral nerve blocks at our institution. Methods A multidisciplinary team (anesthesiologists and informaticians) designed an electronic PONS reporting form within the Electronic Patient Record (Cerner PowerChart(R)), adapting the RA UK pathway to our needs. User feedback and discussions refined the form for usability and comprehensiveness. Results This collaborative approach led to a user-friendly electronic PONS reporting form within the existing clinical workflow. The form facilitates PONS case tracking, enabling future research into risk factors, incidence, and patient management. Conclusions A standardized user-friendly electronic PONS reporting system will improve patient outcomes through better case reporting, follow-up and management. Creating a database of PONS in an institution where a high-volume of nerve blocks are performed is vital for patient safety. This approach can be valuable in circumstances where a high-volume of nerve blocks are performed across multiple sites and for multiple surgical services, ultimately enhancing patient safety.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.017 |
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".