Combined pharyngeal and nasal swab specimens were collected from 100 subjects who presented with a flu-like illness (fever >37.8 degrees C plus 2 of 4 symptoms: cough, myalgia, sore throat, and headache) of <72 hours' duration at 3 different clinics in the province of Quebec, Canada, during the 1998-1999 flu season. The rate of laboratory-confirmed influenza infection was 72% according to cell culture findings and 79% according to the results of multiplex reverse-transcription polymerase chain reaction (RT-PCR) analysis (85%, influenza AH3; 15%, influenza B). All subjects for whom these results were discordant (negative culture and positive PCR) presented with a temperature > or =38.2 degrees C as well as 3 or 4 of the symptoms in the clinical case definition. Stepwise logistic regression showed that cough (odds ratio [OR], 6.7; 95% confidence interval [CI], 1.4-34.1; P=.02) and fever (OR, 3.1; 95% CI, 1.4-8.0; P=.01) were the only factors significantly associated with a positive PCR test for influenza. The positive predictive value, negative predictive value, sensitivity, and the specificity of a case definition including fever (temperature of >38 degrees C) and cough for the diagnosis of influenza infection during this flu season were 86.8%, 39.3%, 77.6%, and 55.0%, respectively.

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Objective To identify the determinants of the intention of physicians to screen for decisional conflict in clinical practice.Background Screening for decisional conflict is one of the key competencies when educating health professionals about shared decision making. Theory-based knowledge about variables predicting their intention to screen for decisional conflict in clinical practice would help design effective implementation interventions in this area.

Background The literature suggests that specific keywords included in summative rotation assessments might be an early indicator of abnormal progress or failure. Objective This study aims to determine the possible relationship between specific keywords on in-training evaluation reports (ITERs) and subsequent abnormal progress or failure. The goal is to create a functional algorithm to identify residents at risk of failure. Methods A database of all ITERs from all residents training in accredited programs at Université Laval between 2001 and 2013 was created. An instructional designer reviewed all ITERs and proposed terms associated with reinforcing and underperformance feedback. An algorithm based on these keywords was constructed by recursive partitioning using classification and regression tree methods. The developed algorithm was tuned to achieve 100% sensitivity while maximizing specificity. Results There were 41 618 ITERs for 3292 registered residents. Residents with failure to progress were detected for family medicine (6%, 67 of 1129) and 36 other specialties (4%, 78 of 2163), while the positive predictive values were 23.3% and 23.4%, respectively. The low positive predictive value may be a reflection of residents improving their performance after receiving feedback or a reluctance by supervisors to ascribe a “fail” or “in difficulty” score on the ITERs. Conclusions Classification and regression trees may be helpful to identify pertinent keywords and create an algorithm, which may be implemented in an electronic assessment system to detect future residents at risk of poor performance.

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