015 Service evaluation: the role of same day emergency care in managing acute neurological presentations
Bibliographic record
Abstract
Background and aim During the COVID-19 pandemic, the neurology department at St Mary’s Hospital had to direct the bulk of its acute work to a newly expanded ‘hot clinic’ running Monday to Friday through Same Day Emergency Care (SDEC). Face to face clinic appointments were also halted and instead triaged to SDEC when examination of the patient was necessary. Patients were referred through a number of routes directly to the neurology consultants or on-call registrar, and subsequently seen on an urgent basis. We were interested in evaluating the types of referrals made to this service as well as their final outcomes. Results A total of 255 patients were seen between 3 March 2021 and 3 August 2021. Approximately a third were from the A&E department and just less than a third were from the Western Eye Hospital, our local ophthalmology A&E. Most referrals were for headache or visual change, and 61% of patients did not need to re-attend SDEC. Importantly, a quarter were discharged home after specialist review, and none required admission from clinic. Thus our emergency service was successful in avoiding admissions while ensuring patients received the care they required in a timely fashion.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".