A Systematic Review on the Synoptic Operative Report Versus the Narrative Operative Report in Surgery
Bibliographic record
Abstract
BACKGROUND: Proper documentation is an essential part of patient safety and quality of care in the surgical field. Surgical procedures are traditionally documented in narrative operative reports which are subjective by nature and often lack essential information. This systematic review will analyze the added value of the newly emerged synoptic reporting technique in the surgical setting. METHODS: A systematic review was conducted to compare the completeness and the user-friendliness of the synoptic operative report to the narrative operative report. A literature search was performed in EMBASE, Ovid MEDLINE, Web of Science, Cochrane CENTRAL, and Google Scholar for studies published up to April 6, 2018. The Newcastle-Ottawa Scale was utilized for the risk of bias assessment of the included articles. PROSPERO registration number was: CRD42018093770. RESULTS: Overall and subsection completion of the operative report was higher in the synoptic operative report. The time until completion of the operative report and the data extraction time were shorter in the synoptic report. One exception was the specific details section concerning the operative procedure, as this was generally reported more frequently in the narrative report. The use of mandatory fields in the synoptic report resulted in more completely reported operative outcomes with completion percentages close to 100%. CONCLUSIONS: The synoptic operative report generally demonstrated a higher completion rate and a much lower time until completion compared to the traditional narrative operative report. A hybrid approach to the synoptic operative report will potentially yield better completion rates and higher physician satisfaction.
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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 | 0.064 | 0.252 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".