Severity of Acute Pain After Breast Surgery Is Associated With the Likelihood of Subsequently Developing Persistent Pain
Bibliographic record
Abstract
OBJECTIVES: Persistent postsurgical pain (PPSP) after surgery for breast cancer has a prevalence of 20% to 52%. Neuroplastic changes may play a role in the aetiology of this pain. The principal objective of this study was to examine the relationship between acute pain after surgery for breast cancer and the likelihood of subsequently developing PPSP. METHODS: Twenty-eight women undergoing surgery for breast cancer completed visual analogue scales for pain and anxiety, the McGill Pain Questionnaire (long form) and the Hospital Anxiety and Depression Scale. Analgesic requirements and adverse effects of analgesic therapy were noted. Quantitative sensory testing was carried out perioperatively using an electrical stimulus, and the sensation perception, pain perception, and pain tolerance thresholds were measured bilaterally at the T4 dermatomes and at the contralateral L5 dermatome. Patients with and without PPSP 3 months postoperatively were compared in terms of these parameters. RESULTS: Eight participants (28.6%) reported PPSP. Those who subsequently developed PPSP reported greater pain scores on the McGill Pain Questionnaire 5 days postoperatively than those that did not (pain rating index, P=0.014; present pain intensity, P=0.032). None had sought medical attention for their persistent pain. Patients with and without PPSP were similar in terms of mental status (anxiety and depression), analgesic consumption, adverse effects of analgesic therapy, and changes on QST. DISCUSSION: Patients who developed PPSP experienced pain of greater intensity on the fifth postoperative day than those that did not.
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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 | 0.001 | 0.006 |
| 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) | 0.004 | 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".