“Free” medical publishing venture gets under way
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Medical staff need to be aware of major incident planningEditor-Last month the BMA warned that too few people in the United Kingdom know how to respond to a terrorist attack. 1 Its concerns about too few senior officials being aware of plans and recommendations to improve preparedness are sensible, but we believe that not enough medical staff are aware of their role in the event of a terrorist attack.We recently carried out a survey in the largest acute NHS trust of the south west of England to assess medical staff's knowledge about the local major incident plan.We sent questionnaires to the 107 doctors in North Bristol NHS Trust with a potential role in the mobile medical team if they were on duty during a major incident.Seventy seven doctors replied (72%).Sixty nine were aware of the existence of the local major incident plan, but only 26 had read part or all of it.Only 11 of the responding doctors were aware of their potential role in the mobile medical team.Of these 11 doctors, only three thought themselves adequately trained for this position, and all three had been trained as medical incident officers.Last year's National Audit Office report highlighted deficiencies in NHS plans to deal with major incidents in England. 2 It recommended upgrading training arrangements.Five months later some doctors are still unaware of their roles in the event of a terrorist attack.As a trust we are currently considering several measures to improve on our results.We suspect, however, that our findings are not unique and encourage other acute trusts to look closely at their staff's knowledge and training and act accordingly.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.024 |
| Insufficient payload (model declined to judge) | 0.067 | 0.003 |
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 it