WED 259 The challenge of mood and cognition screening in hasu
Bibliographic record
Abstract
The National Clinical guideline for stroke, recommends that services for people with stroke should provide screening for mood and cognition within six weeks of stroke using validated tools. In HASU assessing mood and cognition in a timely fashion has been challenging. In 2013 a previous audit showed cognitive screening at 48% and mood screening at only 7%. We instituted a number of measures to improve compliance including education events for MDT staff, a bespoke stroke clerking proforma, to include data collection boxes. We also introduced the briefer ‘YALE questionnaire’ for mood, and the Montreal Cognitive Assessment Tool (MOCA). We also piloted occupational therapy staff using a ‘screening sticker’ in patient notes. A daily MDT was also introduced, primarily to improve patient flow, but also to prompt action planning. Mood screening improved to 92% and cognition screening to 95% on detailed notes audit. These high compliance figures were however not fully reflected on SSNAPP although compliance has improved. SSNAP data input is completed by a single coder, non clinical staff member. We plan to employ a second data clerk and further revise the stroke proforma.
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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 | 0.032 | 0.102 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".