Analgesia nociception index as a tool to assess the effectiveness of paravertebral block in total mastectomy: a prospective cohort study
Bibliographic record
Abstract
Single-injection Paravertebral Block (PVB) is commonly used for analgesia in major breast surgery; however, its sensory effectiveness may be variable. This study investigated whether intraoperative changes in the Analgesia Nociception Index (ANI) are associated with PVB effectiveness. This prospective observational study included 100 women scheduled for total mastectomy. A single-injection PVB was performed preoperatively under ultrasound guidance at the T3 level. Sensory testing was performed from T1 to T10, but block effectiveness was evaluated in the surgical field (T2–T6). PVBs were classified as effective (complete loss of cold sensation in all T2‒T6 dermatomes) or incomplete (partial cold sensation in this range). ANI variations, intraoperative remifentanil consumption, postoperative pain scores, and morphine use were compared. Ninety-three patients were analyzed. PVB was effective in 75% and incomplete in 25%. The mean ANI variation was significantly greater in the effective group (+1.4 ± 10.3) compared to the incomplete group (-11.0±7.1), with a mean difference of 12.4 (95% CI: 8.8 to 16.0; p < 0.0001). Remifentanil consumption was higher in the incomplete group (0.072 ± 0.018 µg.kg −1 .min −1 vs. 0.054 ± 0.008 µg.kg −1 .min −1 ), mean difference 0.018 (95% CI: 0.010 to 0.026; p < 0.0001). Pain score and morphine consumption were significantly higher for patients with incomplete PVB. In this observational study, a significant decrease in ANI values following skin dissection was associated with incomplete PVB. Early ANI monitoring may help identify insufficient regional block during total mastectomy, thus guiding intraoperative analgesic adjustment to improve patient comfort.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".