Chronic pain states have resulted in an over-reliance on opioid pain relievers, which can carry significant risks when used long-term. As such, alternative pain treatments are increasingly desired. Although emerging research suggests that cannabinoids have therapeutic potential regarding pain, results from studies across pain populations have been inconsistent. To provide meta-analytic clarification regarding cannabis’s impact on subjective pain, we identified studies that assessed drug-induced pain modulations under cannabinoid and corresponding placebo conditions. A literature search yielded 25 peer-reviewed records that underwent data extraction. Baseline and end-point data were used to compute standardized effect size estimates (Cohen’s d) across cannabinoid administrations (k = 39) and placebo administrations (k = 26). Standardized effects were inverse-variance weighted and pooled across studies for meta-analytic comparison. Results revealed that cannabinoid administration produced a medium-to-large effect across included studies, Cohen’s d = −0.58, 95% CI (−0.74, −0.43), while placebo administration produced a small-to-medium effect, Cohen’s d = −0.39, 95% CI (−0.52, −0.26). Meta-regression revealed that cannabinoids, β = −0.43, 95% CI (−0.62, −0.24), p < 0.05, synthetic cannabinoids, β = −0.39, 95% CI (−0.65, −0.14), p < 0.05, and sample size, β = 0.01, 95% CI (0.00, 0.01), p < 0.05, were associated with marked pain reduction. These outcomes suggest that cannabinoid-based pharmacotherapies may serve as effective replacement/adjunctive options regarding pain, however, additional research is warranted. Additionally, given demonstrated neurocognitive side-effects associated with some constituent cannabinoids (i.e., THC), subsequent work may consider developing novel therapeutic agents that capitalize on cannabis’s analgesic properties without producing adverse effects.

OBJECTIVE To evaluate effects of blood contamination on dipstick results, specific gravity (SG), and urine protein-to-urine creatinine ratio (UPCR) for urine samples from dogs and cats. SAMPLE Urine samples collected from 279 dogs and 120 cats. PROCEDURES Urine pools were made for each species (dogs [n = 60] and cats [30]). Blood was added to an aliquot of a pool, and serial dilutions were prepared with the remaining urine. Color and dipstick variables were recorded, and SG and UPCR were measured. For cats, 1 set of pools was used; for dogs, 2 sets were used. Comparisons were made between undiluted urine and spiked urine samples for individual colors. Repeated-measures ANOVA on ranks was used to compare dipstick scores and UPCR results; χ tests were used to compare proteinuria categorizations (nonproteinuric, borderline, or proteinuric). RESULTS Any blood in the urine resulted in significantly increased dipstick scores for blood. In both species, scores for bilirubin and ketones, pH, and SG were affected by visible blood contamination. No significant difference for the dipstick protein reagent results was evident until a sample was visibly hematuric. The UPCR was significantly increased in dark yellow samples of both species. Proteinuria categorizations differed significantly between undiluted urine and urine of all colors, except light yellow. CONCLUSIONS AND CLINICAL RELEVANCE Any degree of blood contamination affected results of dipstick analysis. Effects depended on urine color and the variable measured. Microscopic blood contamination may affect the UPCR; thus, blood contamination may be a differential diagnosis for proteinuria in yellow urine samples.

Across three phases, we investigated college students’ perceptions of poor college teaching to develop a typology of poor teaching behaviors. In Phase 1, students generated a list of qualities representative of poor teaching. In Phase 2, another group of students assigned behavioral correspondents to these qualities, resulting in a list of 15 poor teaching qualities and their attendant misbehaviors. Finally, in Phase 3, yet another group of students generated a “top 5” list of qualities and behaviors that their most ineffective teachers have exhibited. Across participants in Phase 3, the top 5 list included, in order, being disrespectful, offering unrepresentative and unfair student learning assessments, having unrealistic expectations for student learning, being less than knowledgeable on course content, and having poor communication skills. These findings provide practical guidance on which particular behaviors teachers should avoid adopting in their instructional repertoires.

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