Pain in Postsurgical Orthopedic Rehabilitation: A Multicenter Study
Bibliographic record
Abstract
OBJECTIVE: The aim of this study was to quantify and characterize pain in patients undergoing lower limb postsurgical orthopedic rehabilitation and to investigate the impact of pain in slowing or interrupting their rehabilitation. DESIGN: The study was designed as a multicenter cross-sectional study. SETTING: The study was set in rehabilitation departments of the Don Gnocchi Foundation. SUBJECTS: The study subjects were the inpatients attending rehabilitation. INTERVENTIONS: There were no interventions used in the study. OUTCOME MEASURES: Pain intensity was measured with a numeric rating scale (NRS); pain characteristics were assessed with the McGill Pain Questionnaire and the ID Pain (able to discriminate nociceptive from neuropathic pain). Quality of life (QoL) was measured with the Short Form 36 Health Status Survey. A semi-structured questionnaire on pain occurrence, impact, and management was administered by the physiotherapist in charge of the patients and by the physician. RESULTS: We studied 139 patients, 82% of whom complained of at least moderate pain (NRS ≥ 3). According to ID pain, 45.6% patients complained of probable (33.8%) or highly probable (11.8%) neuropathic pain. A higher pain intensity was significantly related to the probability of having neuropathic pain (P < 0.001). Patients with more severe pain reported lower physical and mental QoL scores. In 38.6% of cases, pain interfered with the rehabilitation process, and in 18.5% it was the cause of physical therapy discontinuation. CONCLUSIONS: In light of the high occurrence and intensity of pain in the sample, and of the significant impact on the rehabilitation program, clinicians should pay more attention to pain, especially neuropathic pain, in postsurgical patients. Tailored pain pharmacological therapy could possibly improve patient compliance during the rehabilitation process and enhance long-term outcomes.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".