Biased pain evaluation due to automated heuristics driven by symptom uncertainty may undermine pain treatment; medical evidence moderators are thought to play a role in such circumstances. We explored, in this cross-sectional survey, the effect of such moderators (e.g., nurse awareness of patients’ pain experience and treatment) on the agreement between inpatients’ self-reported pain and nurses’ pain ratings using a numerical rating scale. We assessed the mean of absolute difference, agreement (κ-statistics), and correlation (Spearman rank) of inpatients and nurses’ pain ratings and analyzed congruence categories’ (CCs: underestimation, congruence, and overestimation) proportions and dependence upon pain categories for each medical evidence moderator ( analysis). Pain ratings agreement and correlation were limited; the CCs proportions were further modulated by the studied moderators. Medical evidence promoted in nurses overestimation of low and underestimation of high inpatients’ self-reported pain. Knowledge of the negative influence of automated heuristics driven by symptoms uncertainty and medical-evidence moderators on pain evaluation may render pain assessment more accurate. Biased pain evaluation due to automated heuristics driven by symptom uncertainty may undermine pain treatment; medical evidence moderators are thought to play a role in such circumstances. We explored, in this cross-sectional survey, the effect of such moderators (e.g., nurse awareness of patients’ pain experience and treatment) on the agreement between inpatients’ self-reported pain and nurses’ pain ratings using a numerical rating scale. We assessed the mean of absolute difference, agreement (κ-statistics), and correlation (Spearman rank) of inpatients and nurses’ pain ratings and analyzed congruence categories’ (CCs: underestimation, congruence, and overestimation) proportions and dependence upon pain categories for each medical evidence moderator ( analysis). Pain ratings agreement and correlation were limited; the CCs proportions were further modulated by the studied moderators. Medical evidence promoted in nurses overestimation of low and underestimation of high inpatients’ self-reported pain. Knowledge of the negative influence of automated heuristics driven by symptoms uncertainty and medical-evidence moderators on pain evaluation may render pain assessment more accurate.

Objectives: Opioid titration is the first challenging stage for rapid control of moderate/severe cancer pain. Evidence shows that sustained-release formulations may be used for opioid titration. We set a pilot assessment of the efficacy and tolerability of our in-house protocol (continuous and on demand opioids [CoDem]) of the association of sustained-release oxycodone and immediate-release morphine as rescue dose for opioid titration/rotation in opioid-naïve (NAOP, n = 13), tolerant to weak (WOP, n = 20), or strong opioids (STOP, n = 44) in-patients with moderate/severe cancer pain.Methods: Observational and retrospective analysis of cancer in-patients treated for ≥7 days with the CoDem protocol.Outcome measures: Pain intensity (patients self-reported pain with numerical rating scale [NRS] under static [NRSs] and dynamic [NRSd] conditions), amount of drug consumption, opioid adverse effects, and patient satisfaction.Efficacy endpoints: In more than 50 percent of the patients and in <72 hours, steady NRSs and NRSd score reduction of at least two points, NRSs ≤ 3 and NRSd ≤4; and mean daily morphine consumption < mean of one rescue dose and t1:t6 ratio of mean oxycodone daily dose < 1:2.Results: Endpoints were reached within 24 hours both within the sample and subgroups. Only NAOP patients reached NRSd ≤ 4 endpoint within 48 hours. Against moderate and transient adverse effects, most patients (84.4 percent) found pain treatment to be good or excellent.Conclusions: The CoDem protocol was shown to be effective and reasonably tolerated for titration for moderate/severe cancer pain relief in both opioid-naïve or opioid-tolerant cancer in-patients. This pilot assessment warrants prospective and comparative studies with larger samples for more generalized results.

Objectives: Variable prevalence and treatment of breakthrough pain (BTP) in different clinical contexts are partially due to the lack of reliable/validated diagnostic tools with prognostic capability. We report the statistical basis and performance analysis of a novel BTP scoring system based on the naïve Bayes classifier (NBC) approach and an 11-item IQ-BTP validated questionnaire. This system aims at classifying potential BTP presence in three likelihood classes: “High,” “Intermediate,” and “Low.” Methods: Out of a training set of n=120 mixed chronic pain patients, predictors associated with the BTP likelihood variables (Pearson’s χ² and/or Fisher’s exact test) were employed for the NBC planning. Adjusting the binary classification to a three–likelihood classes case enabled the building of a scoring algorithm and to retrieve the score of each predictor’s answer options and the Patient’s Global Score (PGS). The latter medians were used to establish the NBC thresholds, needed to evaluate the scoring system performance (leave-one-out cross-validation). Results: Medians of PGS in the “High,” “Intermediate,” and “Low” likelihood classes were 3.44, 1.53, and −2.84, respectively. Leading predictors for the model (based on score differences) were flair frequency (∆S=1.31), duration (∆S=5.25), and predictability (∆S=1.17). Percentages of correct classification were 63.6% for the “High” and of 100.0% for either the “Intermediate” and “Low” likelihood classes; overall accuracy of the scoring system was 90.9%. Conclusion: The NBC-based BTP scoring system showed satisfactory performance in classifying potential BTP in three likelihood classes. The reliability, flexibility, and simplicity of this statistical approach may have significant relevance for BTP epidemiology and management. These results need further impact studies to generalize our findings.