Dictation Precision: Evaluating Paediatric Oncology Operative Reports as an Impetus for Synoptic Reports
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to audit paediatric surgical oncology dictations to evaluate completeness and ease of data collection and provide a baseline measurement for the implementation of synoptic operative reports (SORs) in a tertiary care setting. METHODS: An audit tool based on Children's Oncology Group (COG) protocols was created to assess the completeness of surgical and tumour-specific data within operative reports. Audited cases included four paediatric tumours of interest (Germ Cell, Wilms, Neuroblastoma, and Hepatoblastoma) between 2010 and 2023. RESULTS: Overall, 71 audits were performed, the majority being Wilms Tumour cases (45.1 %), followed by Neuroblastoma (29.6 %), Germ Cell (18.3 %), and Hepatoblastoma (7.0 %). The average percentage of complete data for operative reports was low; the mean completeness of general oncological information for all tumour types was 66.0 %, and tumour-specific details were 42.0 %. Ovarian Germ Cell Tumour had the highest average percentage of complete data (65.9 %), followed by Wilms Tumour (58.7 %), Testicular Germ Cell Tumour (43.0 %), Neuroblastoma (15.0 %), and Hepatoblastoma (9.5 %). The median data collection time was 6.0 min per audit. The median time from the operation to the transcription of the report was 1.0 days (interquartile range (IQR): 1.0-7.0). CONCLUSION: Narrative operative reports have inadequate completeness, especially concerning tumour-specific factors, which are often essential in accurate diagnosis, and in guiding treatment for children with cancer. SORs could be a solution and aid in the completeness and accessibility of data use. TYPE OF STUDY: Retrospective review. LEVEL OF EVIDENCE (I-V): IV.
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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 | 0.042 | 0.372 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".