AAAPT Diagnostic Criteria for Acute Knee Arthroplasty Pain
Bibliographic record
Abstract
OBJECTIVE: The relationship between preexisting osteoarthritic pain and subsequent post-total knee arthroplasty (TKA) pain is not well defined. This knowledge gap makes diagnosis of post-TKA pain and development of management plans difficult and may impair future investigations on personalized care. Therefore, a set of diagnostic criteria for identification of acute post-TKA pain would inform standardized management and facilitate future research. METHODS: The Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION) public-private partnership with the US Food and Drug Administration (FDA), the American Pain Society (APS), and the American Academy of Pain Medicine (AAPM) formed the ACTTION-APS-AAPM Pain Taxonomy (AAAPT) initiative to address this goal. A multidisciplinary work group of pain experts was invited to conceive diagnostic criteria and dimensions of acute post-TKA pain. RESULTS: The working group used contemporary literature combined with expert opinion to generate a five-dimensional taxonomical structure based upon the AAAPT framework (i.e., core diagnostic criteria, common features, modulating factors, impact/functional consequences, and putative mechanisms) that characterizes acute post-TKA pain. CONCLUSIONS: The diagnostic criteria created are proposed to define the nature of acute pain observed in patients following TKA.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".