What delirium follow-up is routinely offered after elective arthroplasty surgery? A survey of UK and Irish clinicians.
Bibliographic record
Abstract
Background Postoperative delirium (POD) is a serious complication occurring after approximately 17% of elective arthroplasty surgeries. However, it is unclear if any routine clinical follow-up services are available to patients post-discharge. This study aims to determine what routine postoperative delirium screening and documentation processes are in place and what follow-up services are currently offered. Methods A brief online survey of multiple-choice and free-text questions was devised for clinicians in the United Kingdom (UK) and Republic of Ireland (ROI). An email invitation to complete the survey was sent to relevant clinicians in the UK and ROI by non-NHS professional bodies. Twitter was used to highlight and disseminate the survey. Results Of the 43 participating clinicians, 18 (42%) respondents indicated that delirium is routinely screened for after elective arthroplasty and 17 respondents stated that the 4AT tool is used. Most respondents (62%) indicated that delirium is documented upon discharge to patients’ GPs. Only 11 respondents (26%) describe routine clinical follow-up practices. These included a joint arthroplasty clinic, geriatric outpatient department and liaison psychiatry. Conclusions Results of this survey suggest that a) post-arthroplasty delirium screening and documentation is not widespread and b) clinical follow-up services for delirium in the UK and ROI are neither standardised nor routine.
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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".