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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".