Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article

Patient preferences for physical therapy programs after a lower extremity fracture: a discrete choice experiment

Objective To quantify patients’ preferences for physical therapy programmes after a lower extremity fracture and determine patient factors associated with preference variation.Design Discrete choice experiment.Setting Level I trauma centre.Participants One hundred fifty-one adult (≥18 years old) patients with lower extremity fractures treated operatively.Intervention Patients were given hypothetical scenarios and asked to select their preferred therapy course when comparing cost, mobility, long-term pain, session duration, and treatment setting.Main outcome measures A multinomial logit model was used to determine the relative importance and willingness to pay for each attribute.Results Mobility was of greatest relative importance (45%, 95% CI: 40% to 49%), more than cost (23%, 95% CI: 19% to 27%), long-term pain (19%, 95% CI: 16% to 23%), therapy session duration (12%, 95% CI: 9% to 5%) or setting (1%, 95% CI: 0.2% to 2%). Patients were willing to pay US$142 more per session to return to their preinjury mobility level (95$103 to US$182). Willingness to pay for improved mobility was higher for women, patients aged 70 years and older, those with bachelor’s degrees or higher and those living in less-deprived areas. Patients were willing to pay US$72 (95% CI: US$50 to US$93) more per session to reduce pain from severe to mild. Patients were indifferent between formal and independent home therapy (willingness to pay: −US$12, 95$33 to US$9).Conclusions Patients with lower extremity fractures highly value recovering mobility and are willing to pay more for postoperative physical therapy programmes that facilitate returning to their pre-injury mobility level. These patient preferences might be useful when prescribing and designing new techniques for postoperative therapy

Antigliadin Antibodies (AGA IgG) Are Related to Neurochemistry in Schizophrenia

Inflammation may play a role in schizophrenia; however, subgroups with immune regulation dysfunction may serve as distinct illness phenotypes with potential different treatment and prevention strategies. Emerging data show that about 30% of people with schizophrenia have elevated antigliadin antibodies of the IgG type, representing a possible subgroup of schizophrenia patients with immune involvement. Also, recent data have shown a high correlation of IgG-mediated antibodies between the periphery and cerebral spinal fluid in schizophrenia but not healthy controls, particularly AGA IgG suggesting that these antibodies may be crossing the blood–brain barrier with resulting neuroinflammation. Proton magnetic resonance spectroscopy (MRS) is a non-invasive technique that allows the quantification of certain neurochemicals in vivo that may proxy inflammation in the brain such as myoinositol and choline-containing compounds (glycerophosphorylcholine and phosphorylcholine). The objective of this exploratory study was to examine the relationship between serum AGA IgG levels and MRS neurochemical levels. We hypothesized that higher AGA IgG levels would be associated with higher levels of myoinositol and choline-containing compounds (glycerophosphorylcholine plus phosphorylcholine; GPC + PC) in the anterior cingulate cortex. Thirty-three participants with a DSM-IV diagnosis of schizophrenia or schizoaffective disorder had blood drawn and underwent neuroimaging using MRS within 9 months. We found that 10/33 (30%) had positive AGA IgG (≥20 U) similar to previous findings. While there were no significant differences in myoinositol and GPC + PC levels between patients with and without AGA IgG positivity, there were significant relationships between both myoinositol (r = 0.475, p = 0.007) and GPC + PC (r = 0.36, p = 0.045) with AGA IgG levels. This study shows a possible connection of AGA IgG antibodies to putative brain inflammation as measured by MRS in schizophrenia

Implementing stakeholder engagement to explore alternative models of consent: An example from the PREP-IT trials.

IntroductionCluster randomized crossover trials are often faced with a dilemma when selecting an optimal model of consent, as the traditional model of obtaining informed consent from participant's before initiating any trial related activities may not be suitable. We describe our experience of engaging patient advisors to identify an optimal model of consent for the PREP-IT trials. This paper also examines surrogate measures of success for the selected model of consent.MethodsThe PREP-IT program consists of two multi-center cluster randomized crossover trials that engaged patient advisors to determine an optimal model of consent. Patient advisors and stakeholders met regularly and reached consensus on decisions related to the trial design including the model for consent. Patient advisors provided valuable insight on how key decisions on trial design and conduct would be received by participants and the impact these decisions will have.ResultsPatient advisors, together with stakeholders, reviewed the pros and cons and the requirements for the traditional model of consent, deferred consent, and waiver of consent. Collectively, they agreed upon a deferred consent model, in which patients may be approached for consent after their fracture surgery and prior to data collection. The consent rate in PREP-IT is 80.7%, and 0.67% of participants have withdrawn consent for participation.DiscussionInvolvement of patient advisors in the development of an optimal model of consent has been successful. Engagement of patient advisors is recommended for other large trials where the traditional model of consent may not be optimal

Implementing stakeholder engagement to explore alternative models of consent: An example from the PREP-IT trials

Introduction: Cluster randomized crossover trials are often faced with a dilemma when selecting an optimal model of consent, as the traditional model of obtaining informed consent from participant's before initiating any trial related activities may not be suitable. We describe our experience of engaging patient advisors to identify an optimal model of consent for the PREP-IT trials. This paper also examines surrogate measures of success for the selected model of consent. Methods: The PREP-IT program consists of two multi-center cluster randomized crossover trials that engaged patient advisors to determine an optimal model of consent. Patient advisors and stakeholders met regularly and reached consensus on decisions related to the trial design including the model for consent. Patient advisors provided valuable insight on how key decisions on trial design and conduct would be received by participants and the impact these decisions will have. Results: Patient advisors, together with stakeholders, reviewed the pros and cons and the requirements for the traditional model of consent, deferred consent, and waiver of consent. Collectively, they agreed upon a deferred consent model, in which patients may be approached for consent after their fracture surgery and prior to data collection. The consent rate in PREP-IT is 80.7%, and 0.67% of participants have withdrawn consent for participation. Discussion: Involvement of patient advisors in the development of an optimal model of consent has been successful. Engagement of patient advisors is recommended for other large trials where the traditional model of consent may not be optimal