Characterization of endotoxins on orthopaedic fixation screws, using physicochemical surface analyses
Bibliographic record
Abstract
The objective of this study was to determine if surface analysis techniques could be used to detect endotoxin on stainless steel malleolus screws. New malleolus screws were compared to ones that had been coated in purified lipopolysaccharide (LPS) or Artificial Test Soil (ATS) containing lipopolysaccharide. X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), and time-of flight secondary ion mass spectrometry (TOF-SIMS) were used to assess the fixation screws surface. Organic material was visualized on the LPS and ATS-LPS inoculated screws but not on the new unsoiled screws. This was further supported by the peaks observed at masses between 40 and 100 D in TOF-SIMS spectra of the LPS and ATS-LPS inoculated screws. After deconvolution of N1s high resolution XPS spectra, the LPS inoculated screws showed amide groups whereas the ATS-LPS inoculated screws showed predominantly nitroso groups (C-NO). Our data demonstrate that surface analysis can be used to detect organic residuals present on fixation screws. The XPS data confirmed that LPS reacted predominantly with positively charged surface metallic ions (Fe and Cr), whereas proteins reacted with the surface oxide layer of fixation screws, forming C-NO groups. The application of these surface analysis techniques will be helpful in determining if the reprocessing of such items results in an accumulation of organic material that might lead to aseptic loosening, when implanted. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:240-247, 2017.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".