Hot off the press: Stop fallin’—Geriatric fall prevention in the emergency department
Bibliographic record
Abstract
Close to three million adults aged 65 and over visit American emergency departments (EDs) annually after a fall.1 Approximately 20% of falls result in injuries and, as a result, falls are the most common cause of traumatic injury resulting in older adults presenting to the ED.2 Among this age group, falls are the leading cause of traumatic mortality in this age group.3-5 The article then also describes a consensus conference and involvement of patient advocates in developing five research priorities for the field. This is a scoping review, which means that its aim is to provide a roadmap of the literature on a particular topic and identify key concepts and gaps in the research base. There is a specific PRISMA tool for quality assessment of scoping reviews9 that we have applied. The process in this paper was clearly described and could be replicated; however, quality of the data was limited by the paucity of agreed upon definitions around this topic (including what constitutes a “fall”). This is clear from the level of disagreement between reviewers about what should be included in the second review—the kappa, a measurement of inter-rater reliability was only 0.12 on a scale of −1 to 1, where −1 represents perfect disagreement and 1 represents perfect agreement. The authors justifiably did not attempt a statistical synthesis of the data because the definitions of so many interventions and outcomes varied widely. This scoping review included 32 studies that addressed fall prevention in the ED: three meta-analyses and 23 RCTs, with a total of 571,071 patients. Studies were from 11 countries, 1999–2019, with follow-up from 1 to 18 months. Interventions included falls risk assessment, physical rehabilitation sessions, preventive education, educational guidelines, follow-up with nurse practitioner or physical therapist, and alert devices. Most used recurrent falls as the outcome although anxiety over falls, functional ability, and QALYs also featured. Of these studies, 17 addressed risk stratification and falls care plans: four meta-analyses and eight RCTs, with a total of at least 17,232 patients. Studies were from nine countries, 2011–2018, with follow-up from 6 to 12 months. Eleven screening instruments were identified with interventions including educational, physical therapy, follow-up calls, discharge planning, and home visits. Most used recurrent falls as the outcome. Historically, EM has not screened for post-ED fall-risk https://onlinelibrary.wiley.com/doi/10.1111/acem.12332 in @AcademicEmerMed. More recently, @MauraKennedyMD demonstrated low screening rates even among @EmergencyDocs #GEDA sites https://linkinghub.elsevier.com/retrieve/pii/S0196-0644(21)00513-8 - 48% affirmative #SGEMHOP response seems high. Maybe the poll should have said “a fall risk strategy that is more than a bracelet or different colored socks.” Then see if it is still 48% of EDs! Agree Dr. Southerland. There's a big difference in asking a fall risk question in triage +/− “fall risk” bracelet and putting the procedures & policies in place to encourage safe mobility and address fall risk factors (medication, home safety, appropriate assistive device use). Just one of many great reasons to have a physical therapist on staff in the ED! Fall assessment and prevention is our jam…(among other things). #emergencyPT #ePT #PTinED #paperinapic by @schmadeline and @kirstychallen. Patients may (or may not) benefit from falls screening and interventions. There is inadequate evidence to support a specific tool or intervention across the board but it is likely that multifactorial interventions are most effective.
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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".