National Audit of Meningitis Management (NAMM): a National Infection Trainee Collaborative for Audit and Research (NITCAR) audit of adherence to the 2016 UK joint specialist societies’ guideline on the diagnosis and management of acute meningitis in adults
Bibliographic record
Abstract
Background Bacterial meningitis has significant mortality but frontline doctors will see it infrequently. Therefore, UK guidance on meningitis in adults, with auditable standards, was revised in 2016. We undertook a national audit to assess adherence to the guidelines. Methods Patients with community acquired meningitis were identified through coding or laboratory data. Audit standards, including immediate management, diagnostics and treatment, were evaluated by notes review. Results Notes from 1472 patients with meningitis were reviewed – 309/1472 (21%) had bacterial aetiology, 615/1472 (42%) viral, 548/1472 (37%) unidentified aetiology. Only 50% of patients had blood cultures taken within one hour of admission and just 2% had a lumbar puncture (LP) within the first hour. 27% received antibiotics within one hour. Most patients received ceftriaxone or cefotaxime but only 37% of over-60s received empirical anti-listeria antibiotics. 26% of patients who had antibiotics were given adjunctive steroids. Half had CSF microscopy within two hours of LP. Less than a third had pneumococcal and/or meningococcal PCR on cerebrospinal fluid. Only 44% had an HIV test. 62% had unnecessary neuroimaging before LP. Overall mortality was 3% - 16% in pneumococcal disease and 8% in meningococcal meningitis. There was a trend toward improved survival in patients with pneumococcal meningitis who received dexamethasone [85/96 (88%)] compared to those who did not [57/73 (78%)] (p=0.066). Conclusions Adherence to the meningitis guidelines is inadequate, potentially compromising patient safety. Improvements in guideline dissemination, novel educational resources and clinician and patient engagement are required if we are to increase guideline adherence and improve outcome.
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 | 0.082 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 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".