Bacterial Deoxyribonucleic Acid Is Often Present in Failed Revision Anterior Cruciate Ligament Reconstructions
Bibliographic record
Abstract
PURPOSE: To determine whether bacterial DNA will be detectable by polymerase chain reaction (PCR) in torn graft tissue at the time of revision anterior cruciate ligament reconstruction (ACLR). METHODS: A total of 31 consecutive revision ACLR cases from 1 center from 2014-2016 were recruited. No patients had clinical signs of infection on presentation. Torn graft tissue was obtained in revision cases and subjected to clinical culture and PCR analysis with a universal bacterial primer. Fluorescence microscopy was used to confirm the presence of a biofilm. We obtained negative control samples of water open to air on the field and excess primary ACLR graft tissue, as well as torn native ligament, to evaluate for PCR positivity due to environmental contamination. RESULTS: Clinical cultures were positive (coagulase-negative Staphylococcus) in 1 revision case (3%, 1 of 31). Bacterial DNA was detectable in most revision ACLR cases (87.0%, 27 of 31), and there was a low rate of PCR positivity in negative control samples of water open to air (0%, 0 of 3), excess primary ACLR graft tissue after passage (20%, 1 of 5), or native torn ligament (20%, 1 of 5). Bacterial biofilm presence on failed graft tissue as well as monofilament suture was visually confirmed with fluorescence microscopy. CONCLUSIONS: Bacterial DNA is frequently present in failed ACLR grafts, with high rates of DNA detection by PCR but low culture positivity. LEVEL OF EVIDENCE: Level IV, case series.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".