39: Contemporary Challenges Faced by Canadian Physicians in ADHD Management
Bibliographic record
Abstract
The management of ADHD remains filled with areas of uncertainties for specialists involved in patient care. To identify the challenges in ADHD management in Canada. Between December 2013 and June 2014, 40 specialists and 10 primary care physicians (PCPs) from across Canada participated in the online REFLECT survey (pilot project) that assessed their perceptions regarding their ADHD practice. Actual practice was evaluated via 414 chart audits – three visits/patient over five months. The cumulative loss to follow up (n=414) was 38 by Visit 1 and 119 by Visit 2. Paediatricians, child/adolescent psychiatrists and PCPs comprised 48%, 22% and 20% of the respondents, respectively; 76% and 48% had been in practice and treating ADHD for >20 years, respectively. The survey revealed that most respondents perceived themselves to closely follow best practices and guidelines; however, while 78% reported using rating scales to monitor response and all prioritized duration of efficacy as predominantly dictating the choice of pharmacotherapy, the review of the chart audits suggest otherwise. Figure 1 summarises the perceived vs actual use of rating scales in establishment of patient-defined treatment goals at each visit stratified by speciality. Pharmacotherapy was changed for 40% and 37% of patients at Visits 1 and 2, respectively. Suboptimal symptom control resulted in intensification (up-titration; Visit 1 73%; Visit 2 68% or addition of another agent; Visit 1 44%; Visit 2 30%) or discontinuations without replacement (Visit 1 43%, Visit 2 32%). Side effects triggered down-titration for 55% and 45% of patients at both Visits 1 and 2, respectively. The pilot phase of the REFLECT survey and chart audit suggest wide inconsistencies in the assessment and management of ADHD in Canadian specialist and primary care practices that are likely contributing to the persistent care gap. The national IRB approved REFLECT survey study should confirm these findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.021 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".