Abstracts from the 4th Annual Student Medical Summit
Bibliographic record
Abstract
Anonymized data of a random sample of 10% of all children aged 1-5 who had general anaesthesia in 2018 in Cork University Hospital was provided by the Hospital Inpatient Enquiry.Top performance indicators as set out by the PAMoC were collected and compared to international standards.These included fasting times, post-operative nausea/vomiting or unplanned admission after day-case surgery.Results 16 departments responded to the survey (response rate 57%), representing both model 3 and model 4 hospitals.Overall, 93.75% felt the model of care had not meaningfully changed or influenced practice in their department.Only 50% of hospitals have a lead paediatric anaesthesiologist and of these, only 31% lead paediatric anaesthesiologists undertake a paediatric list weekly.In terms of quality improvement, 12 (75%) departments are not routinely recording performance indicators for paediatric anaesthesia.65 patients were included in the audit.Mean fasting time for this sample was 12 hours.Post-operative nausea and vomiting was identified in 9.7% of the sample.The unplanned admission rate was 18%.In comparison to other specialities, children undergoing orthopaedic surgery were significantly more likely to have an unplanned admission (p< 0.003).73% of unplanned admissions were orthopaedic cases. ConclusionsThis study indicates the PAMoC has not been effectively implemented in non-specialist Irish public hospitals, with comparatively high fasting times [2] and unplanned admissions [3] highlighting an area for future study and quality improvement to deliver the best quality anaesthesia care for children in Ireland References 1. HSE Model of Care for Paediatric Anaesthesia.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.272 | 0.108 |
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".