“One size does not fit all”: translating network meta-analysis findings into personalized post-cesarean analgesia

Dear Editor, We read with great interest the comprehensive network meta-analysis by Jing et al comparing 17 analgesic modalities after cesarean section[1]. The authors are to be commended for their rigorous synthesis of 110 randomized trials and for clarifying nuanced trade-offs – especially the context-dependent superiority of quadratus lumborum block III (QLB III; with opioid rescue) and transversalis fascia plane block (TFP; with non-opioid rescue)[1]. However, we caution against interpreting “QLB III is the safest and most effective” as a universal recommendation[1]. In clinical practice, optimal analgesia must be individualized, not standardized[2]. Three critical considerations temper broad adoption: First, patient heterogeneity matters. Factors such as body habitus, urgency of surgery, opioid tolerance, and breastfeeding intentions significantly influence both efficacy and risk. For instance, QLB III may underperform in obese parturients due to ultrasound visualization challenges – yet obesity affects over 30% of pregnant women in many regions. Second, technical feasibility varies widely across settings. QLB III requires advanced ultrasound skills, specialized training, and additional procedural time[1]. In low-resource or non-academic hospitals, wound infiltration or Petit transversus abdominis plane block (Petit-TAP) may offer more reliable, albeit modest, benefits without increasing procedural complexity or delaying discharge. Third, “best” is goal-dependent. As Jing et al note, intrathecal morphine provides superior analgesia but at the cost of pruritus and nausea[1]. If the priority is early ambulation, uninterrupted mother–infant bonding, or successful breastfeeding – core elements of enhanced recovery after cesarean[3] – a moderately effective but low-sedation block may be preferable – even if it scores lower on composite rankings. We thus urge integration of these findings into shared decision-making frameworks rather than algorithmic mandates. Future work could develop risk-stratified analgesic pathways or digital decision aids that account for patient preferences, institutional capabilities, and local complication profiles – thereby ensuring that high-level evidence truly serves all patients, not just those in ideal trial conditions. We adhered to contemporary guidance on transparency in reporting the use of artificial intelligence in research (TITAN guideline)[4].