Assessing the TNM Classification for Periprosthetic Joint Infections of the Knee: Predictive Validity for Functional and Subjective Outcomes
Bibliographic record
Abstract
Background: Periprosthetic joint infection (PJI) following knee arthroplasty can significantly compromise patient mobility and quality of life. The newly proposed TNM classification system, adapted from oncology, categorizes PJI severity but has not yet been correlated with both subjective and objective outcomes post PJI treatment. Objective: This study evaluates the applicability of the TNM classification system for predicting outcomes in knee PJI revision surgeries. Methods: We conducted a retrospective analysis of 108 patients who underwent revision surgeries for knee PJI at our institution from January 2012 to January 2023. We assessed the correlation between the TNM classification and postoperative outcomes using the Knee Society Score (KSS) function and knee score, as well as the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). Results: The TNM classification demonstrated that higher ‘T’ stages were significantly associated with worse functional and subjective outcomes. The ‘N’ classification had limited predictive value, likely due to treatment adjustments based on pathogen type. The ‘M’ classification correlated with functional outcomes but not with subjective scores, suggesting that patients with more severe preoperative comorbidities might adjust their expectations. Conclusions: While the TNM classification shows potential, its current form as a prognostic tool in PJI management is limited. Enhancing the ‘T’ component, coupled with the integration of a validated morbidity score such as the CCI could improve its prognostic value. Despite its shortcomings, the TNM system may still provide valuable prognostic insights for both patients and surgeons in tackling complex PJI.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".