Dietetic practice by Australian and Canadian dietitians with people with Parkinson's disease
Bibliographic record
Abstract
Although Parkinson’s disease (PD) is a complex disease for which appropriate nutrition management is important, limited evidence is currently available to support dietetic practice. Existing PD-specific guidelines do not span all phases of the Nutrition Care Process (NCP). This study aimed to document PD-specific nutrition management practice by Australian and Canadian dietitians. DAA members and PEN subscribers were invited to participate in an online survey (late 2011). Eighty-four dietitians responded (79.8% Australian). The majority (70.2%) worked in the clinical setting. Existing non-PD guidelines were used by 52.4% while 53.6% relied on self-initiated literature reviews. Weight loss/malnutrition, protein intake, dysphagia and constipation were common issues in all NCP phases. Respondents also requested more information/evidence for these topics. Malnutrition screening (82.1%) and assessment (85.7%) were routinely performed. One-third did not receive referrals for weight loss for overweight/obesity. Protein intake meeting gender/age recommendations (69.0%), and high energy/high protein diets to manage malnutrition (82.1%) were most commonly used. Constipation management was through high fibre diets (86.9%). Recommendations for spacing of meals and PD medications varied with 34.5% not making recommendations. Nutritional diagnosis (70.2%) and stage of disease (61.9%) guided monitoring frequency. Common outcome measures included appropriate weight change (97.6%) and regular bowel movements (88.1%). With limited PD-specific guidance, dietitians applied best available evidence for other groups with similar issues. Dietitians requested evidence-based guidelines specifically for the nutritional management of PD. Guideline development should focus on those areas reported as commonly encountered. This process can identify the gaps in evidence to guide future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".