Can preoperative quantitative sensory testing predict persistent post-operative knee pain following total knee replacement?: A systematic review1
Bibliographic record
Abstract
OBJECTIVE: To investigate whether pre-operative Quantitative Sensory Testing (QST) can identify patients who experience persistent post-operative knee pain following Total Knee Replacement (TKR). DATA SOURCES: PubMed, EMBASE, CINAHL, EBSCO and grey literature. STUDY SELECTION: 1056 studies were retrieved. The title and abstracts were screened by two independent reviewers, of which 45 were retrieved for full text analysis and 16 studies were included. Studies of any design were included if they recruited adults who underwent TKR; completed any component of the German Research Network on Neuropathic Pain QST or conditioned pain modulation testing preoperatively and assessed post-surgical joint pain using a self-reported outcome measure at a minimum of three months post TKR. DATA EXTRACTION: Data was independently extracted by two researchers. Disagreements were resolved through consensus. The extracted data was recorded in a predefined spreadsheet. Domains included demographic data, type and site of QST, pain outcome measure, follow up duration, statistical methods and associative data. Two independent reviewers assessed the quality of studies using Quality in Prognosis risk of bias tool and the certainty of evidence using the GRADE framework. DATA SYNTHESIS: Sixteen cohort studies met the eligibility criteria (n = 2051 patients). Data was analysed narratively because of the heterogeneity across the QST procedures (mechanical and thermal detection and pain thresholds, conditioned pain modulation and temporal summation of pain), measures of reporting pain (Western Ontario and McMaster Universities Osteoarthritis Index, visual analogue scale and numeric pain rating score) and follow up time points (3 to 18 months). CONCLUSIONS: Due to the heterogeneity and low-moderate quality studies included, it remains unclear whether QST can identify patients who are likely to experience persistent postoperative joint pain following TKR.
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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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".