Bibliographic record
Abstract
The aim of this study was to re-assess whether the use of a 'one-knife technique' can be considered as safe as the alternative practice of using separate skin and inside knives for elective orthopaedic surgery. A total of 609 knife blades from 203 elective orthopaedic operations, with equal numbers of skin, inside and control blades, were cultured using direct and enrichment media. We found 31 skin blades (15.3%), 22 inside blades (10.8%), and 13 control blades (6.4%) gave bacterial growth. Of the 31 contaminated skin blades only three (9.7%) had growth of the same organism as found on the corresponding inside blade. It is not known whether contamination of deeper layers in the remaining 90% was prevented by changing the knife after the skin incision. The organisms cultured were predominantly coagulase-negative staphylococci and proprionibacterium species; both are known to be the major culprits in peri-prosthetic infection. Our study suggests that the use of separate skin and inside knives should be maintained as good medical practice, since the cost of a single deep infection in human and financial terms can be considerable.
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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".