50 Cognitive Assessment of Patients As Mandatory Part of MDT in A Community Rehabilitation Hospital
Bibliographic record
Abstract
Abstract Introduction Undiagnosed underlying cognitive issues have an impact on progression with rehabilitation. Early diagnosis of these is beneficial to the patient as it can offer them early treatment and advance planning for their future care. Methods Patients transferred to our rehabilitation wards from other specialities often do not have a routine cognitive assessment done. This has a negative impact on their rehabilitation goals and discharge planning. We hence routinely assessed cognition for all the patients transferred to our rehabilitation unit across two sites for 3 months. In the weekly MDT, we discussed in detail the cognition of each patient, taking into account not only the doctor’s view, but also nursing and therapists. Once a concern was raised, we investigated them fully with blood tests, imaging and MOCA or ACE-R. Results 56 patients were diagnosed as having cognitive issues. Average age was 81.67 years. Of them 32.14%were from surgical specialities and the rest from other sub-specialities of medicine. In the MDT cognitive concerns were raised 73.2% by therapists, 66.1%by nurses and 60.7% by doctors. Of the concerns raised, 87.4% of patients were diagnosed with some form of underlying dementia or cognitive impairment. 55.4% were started on treatment. Remaining was either palliative deemed unsuitable for treatment or needed more detailed input from community psychiatry team on discharge. 75% were for follow up with the mental health team on discharge, 7.14% by the Parkinson’s specialist and the rest by the own GP. All diagnosis was notified to the patient, next-of-kin and the GP Conclusions Routine multidisciplinary approach to cognitive assessment helps us in new and prompt diagnosis of dementia, offer appropriate treatment and plan ahead for the future.
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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".