Can trainees design and deliver a national audit of epistaxis management? A pilot of a secure web-based audit tool and research trainee collaboratives
Bibliographic record
Abstract
OBJECTIVE: To investigate the feasibility of a national audit of epistaxis management led and delivered by a multi-region trainee collaborative using a web-based interface to capture patient data. METHODS: Six trainee collaboratives across England nominated one site each and worked together to carry out this pilot. An encrypted data capture tool was adapted and installed within the infrastructure of a university secure server. Site-lead feedback was assessed through questionnaires. RESULTS: Sixty-three patients with epistaxis were admitted over a two-week period. Site leads reported an average of 5 minutes to complete questionnaires and described the tool as easy to use. Data quality was high, with little missing data. Site-lead feedback showed high satisfaction ratings for the project (mean, 4.83 out of 5). CONCLUSION: This pilot showed that trainee collaboratives can work together to deliver an audit using an encrypted data capture tool cost-effectively, whilst maintaining the highest levels of data quality.
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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 | 0.060 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".