Postoperative deep shoulder infections following rotator cuff repair
Bibliographic record
Abstract
Rotator cuff repair (RCR) is one of the most commonly performed surgical procedures in orthopaedic surgery. The reported incidence of deep soft-tissue infections after RCR ranges between 0.3% and 1.9%. Deep shoulder infection after RCR appears uncommon, but the actual incidence may be higher as many cases may go unreported. Clinical presentation may include increasing shoulder pain and stiffness, high temperature, local erythema, swelling, warmth, and fibrinous exudate. Generalized fatigue and signs of sepsis may be present in severe cases. Varying clinical presentation coupled with a low index of suspicion may result in delayed diagnosis. Laboratory findings include high erythrocyte sedimentation rate and C-reactive protein level, and, rarely, abnormal peripheral blood leucocyte count. Aspiration of glenohumeral joint synovial fluid with analysis of cell count, gram staining and culture should be performed in all patients suspected with deep shoulder infection after RCR. The most commonly isolated pathogens are Propionibacterium acnes , Staphylococcus epidermidis , and Staphylococcus aureus . Management of a deep soft-tissue infection of the shoulder after RCR involves surgical debridement with lavage and long-term intravenous antibiotic treatment based on the pathogen identified. Although deep shoulder infection after RCR is usually successfully treated, complications of this condition can be devastating. Prolonged course of intravenous antibiotic treatment, extensive soft-tissue destruction and adhesions may result in substantially diminished functional outcomes.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".