An audit of the management of heart murmurs on the postnatal wards
Bibliographic record
Abstract
BACKGROUND: Investigation and management of neonatal heart murmurs varies widely and is dependent on local resources. In order to standardise the management of heart murmurs in our hospital a guideline (based on clinical examination with selective cardiology review) was introduced. AIMS: To establish adherence to and safety of the guideline; to review workload implications and to define the causes of neonatal heart murmurs in our population. METHODS: Patients were prospectively identified over a 2-year period (August 2006 to July 2008). Case notes were reviewed and examination findings, investigations, follow up and diagnosis recorded. RESULTS: 89 babies were identified. The guideline was generally well adhered to. In total 51 (57%) of babies were referred for cardiology assessment. In 40 babies this assessment included an echocardiogram. 30 babies (34%) had an underlying cardiac malformation: 25 were identified before discharge home. 15/30 (50%) of the babies with a cardiac malformation remain under cardiology follow up at the age of 1 year. No baby discharged from follow up without cardiology review subsequently presented with a cardiac problem. CONCLUSION: A significant minority of babies with a heart murmur have an underlying cardiac malformation. Our guideline appears to ensure the timely identification of these babies and rationalises our use of specialist services.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".