1805 The importance of ongoing awareness and education for Lying and Standing blood pressure (LSBP) during hospital admissions
Bibliographic record
Abstract
Abstract Introduction Inpatient falls remain a huge problem in hospital, causing significant injuries to patients and are an avoidable cost to the NHS. Therefore, the National Audit of Inpatient Falls (2015-2017) set out key recommendations for management of falls, including the measurement of LSBP within 3 days of hospital admission. Method Our project was conducted in a major acute teaching hospital in North West London across three geriatric wards. Our aim was to improve the measurement of LSBP and correct documentation across the wards in line with the NAIF guidelines. We excluded patients unable to mobilise to standing with support, patients too unwell or unable to follow instructions and actively dying patients. Prior to any intervention, we found that only 24% of patients had LSBP performed within three days of admission. We focused our intervention in raising education and awareness across our staff. We arranged weekly reminders during MDT meetings, created posters and organised twice monthly teaching sessions, including one to one, on how to document correctly electronically. Results After one month of intervention, 73% of patients had LSBP as part of the ward round plan and almost half of patients had it correctly recorded on our system. After 4 months, we reaudited our project and found that only 32% of patients had LSBP appropriately recorded. This significant decrease can be explained by the changeover of junior doctors and emphasises the need of a more sustainable change. Conclusion Our goal is making LSBP part of a routine preadmission checklist when appropriate. We are currently working on making changes to our electronic patient record (EPR) to facilitate documentation to members of staff. This includes a new falls assessment tool and the newly incorporation of Smartzone feature on EPR. This will allow staff to put non-critical jobs in the workflow showing a less intrusive alert until completed.
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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 | 0.002 | 0.021 |
| 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 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".