Physical therapy after breast cancer surgery improves range of motion and pain over time
Bibliographic record
Abstract
ABSTRACT Treatment of breast cancer can impair range of motion (ROM) and cause homolateral upper limb pain (UL). This study aimed to compare the ROM, intensity and characterization of UL homolateral pain between the 1st, 10th and 20th sessions of physiotherapy, besides correlating these variables. A clinical trial self-controlled study involving 49 women after mastectomy or quadrantectomy with pain complaint on UL was conducted. ROM was evaluated by goniometry and contralateral UL was adopted as control. The intensity of pain was evaluated by the visual analogue scale (VAS) and characterized by the McGill questionnaire, obtaining the number of words chosen (NWC) and the pain evaluation index (PRI). The ROM of the homolateral UL increased significantly over the 20 sessions. Comparing the homolateral UL with the control, only the abduction did not improve significantly after the 20th session. Pain intensity, PRI and NWC reduced significantly between 1st and 10th and between 1st and 20th sessions. The sensory and evaluative categories also decreased significantly. We observed a significant correlation between VAS, PRI and NWC in the 10th and 20th sessions. Physiotherapy increased ROM, reduced pain in the homolateral UL, and fewer words were chosen to characterize the pain. Significant improvements were observed at the beginning of treatment, but with additional gains over time. Exercises for bilateral flexion, abduction, and external rotation should be emphasized.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".