MétaCan
Menu
Back to cohort
Record W4391233069 · doi:10.1093/ageing/afad246.007

1805 The importance of ongoing awareness and education for Lying and Standing blood pressure (LSBP) during hospital admissions

2024· article· en· W4391233069 on OpenAlexaff
David Bendahan, Cedar L. Mitchell, S Chauduri, Jeffrey J. Wing, Brian Bird, Sumna Safeer, Sanjukta Hota

Bibliographic record

VenueAge and Ageing · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Systems and Practices
Canadian institutionsSt Mary's Hospital Centre
Fundersnot available
KeywordsLyingMedicineBlood pressureIntensive care medicineEmergency medicineGerontologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.059
GPT teacher head0.440
Teacher spread0.380 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueAge and AgeingSame topicHealthcare Systems and PracticesFrench-language works237,207