Fluid lavage in patients with open fracture wounds (FLOW): an international survey of 984 surgeons
Bibliographic record
Abstract
BACKGROUND: Although surgeons acknowledge the importance of irrigating open fracture wounds, the choice of irrigating fluid and delivery pressure remains controversial. Our objective was to clarify current opinion with regard to the irrigation of open fracture wounds. METHODS: We used a cross-sectional survey and a sample-to-redundancy strategy to examine surgeons' preferences in the initial management of open fracture wounds. We mailed this survey to members of the Canadian Orthopaedic Association and delivered it to attendees of an international fracture course (AO, Davos, Switzerland). RESULTS: Of the 1,764 surgeons who received the questionnaire, 984 (55.8%) responded. In the management of open wounds, the majority of surgeons surveyed, 676 (70.5%), favoured normal saline alone. Bacitracin solution was used routinely by only 161 surgeons (16.8%). The majority of surgeons, 695 (71%) used low pressures when delivering the irrigating solution to the wound. There was, however considerable variation in what pressures constituted high versus low pressure lavage. The overwhelming majority of surgeons, 889 (94.2%), reported they would change their practice if a large randomized controlled trial showed a clear benefit of an irrigating solution - especially if it was different from the solution they used. CONCLUSION: The majority of surgeons favour both normal saline and low pressure lavage for the initial management of open fracture wounds. However, opinions varied as regards the comparative efficacy of different solutions, the use of additives and high versus low pressure. Surgeons have expressed considerable support for a trial evaluating both irrigating solutions and pressures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".