THE ELECTROENCEPHALOGRAM: MISUSED AND MISUNDERSTOOD?
Bibliographic record
Abstract
Electroencephalograms (EEG9s) are a popular non-invasive test with a minimal side effect profile. However, previous studies have found that more than half of the requests for EEG9s are inappropriate. There is a two-fold importance for ensuring EEG9s are used effectively. Firstly, despite being relatively inexpensive compared with other tests, an inappropriate investigation is a waste of time and resources. Secondly, there are limitations to the capabilities of the EEG. An inappropriate request can lead to a false positive result and incorrect diagnosis with life changing consequences. An audit conducted in St George9s Hospital, London in 2003–2004 found that over a quarter of EEG requests were “inappropriate” and rarely altered management. We audited 35 EEG requests made at the Manchester Royal Infirmary from 01/06/2013–31/07/2013. We used case notes and discharge letters to help assess whether EEG9s were requested appropriately, whether they altered management and whether they were performed within the national target of four weeks. We found 71.4% of EEG requests were inappropriate, similar to the SGH audit. As there has been little improvement in 7 years, further discussion and clearer guidelines on EEG requesting are needed.
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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 | 0.006 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".