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Record W4389438634 · doi:10.2106/jbjs.oa.23.00077

Assessment of Residual Pain and Dissatisfaction in Total Knee Arthroplasty

2023· article· en· W4389438634 on OpenAlexaboutno aff
Omar Musbahi, Jamie E. Collins, Heidi Y. Yang, Faith Selzer, Antonia F. Chen, Jeffrey K. Lange, Elena Losina, Jeffrey N. Katz

Bibliographic record

VenueJBJS Open Access · 2023
Typearticle
Languageen
FieldMedicine
TopicTotal Knee Arthroplasty Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineMinimal clinically important differenceOsteoarthritisWOMACPhysical therapyResidualProspective cohort studyMetric (unit)SurgeryRandomized controlled trialAlternative medicineMathematicsAlgorithm

Abstract

fetched live from OpenAlex

Background: Residual pain after total knee arthroplasty (TKA) refers to knee pain after 3 to 6 months postoperatively. The estimates of the proportion of patients who experience residual pain after TKA vary widely. We hypothesized that the variation may stem from the range of methods used to assess residual pain. We analyzed data from 2 prospective studies to assess the proportion of subjects with residual pain as defined by several commonly used metrics and to examine the association of residual pain defined by each metric with participant dissatisfaction. Methods: We combined participant data from 2 prospective studies of TKA outcomes from subjects recruited between 2011 and 2014. Residual pain was defined using a range of metrics based on the WOMAC (Western Ontario and McMaster Universities Osteoarthritis Index) pain score (0 to 100, in which 100 indicates worst), including the minimal clinically important difference (MCID) and patient acceptable symptom state (PASS). We also examined combinations of MCID and PASS cutoffs. Subjects self-reported dissatisfaction following TKA, and we defined dissatisfied as somewhat or very dissatisfied at 12 months. We calculated the proportion of participants with residual pain, as defined by each metric, who reported dissatisfaction. We examined the association of each metric with dissatisfaction by calculating the sensitivity, specificity, positive predictive value, and Youden index. Results: We analyzed data from 417 subjects with a mean age (and standard deviation) of 66.3 ± 8.3 years. Twenty-six participants (6.2%) were dissatisfied. The proportion of participants defined as having residual pain according to the various metrics ranged from 5.5% to >50%. The composite metric Improvement in WOMAC pain score ≥20 points or final WOMAC pain score ≤25 had the highest positive predictive value for identifying dissatisfied subjects (0.54 [95% confidence interval, 0.35 to 0.71]). No metric had a Youden index of ≥50%. Conclusions: Different metrics provided a wide range of estimates of residual pain following TKA. No estimate was both sensitive and specific for dissatisfaction in patients who underwent TKA, underscoring that measures of residual pain should be defined explicitly in reports of TKA outcomes. Level of Evidence: Therapeutic Level III. See Instructions for Authors for a complete description of levels of evidence.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.040
GPT teacher head0.403
Teacher spread0.363 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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