Complex Chronic Diseases Program: Program Description & Health Outcomes Assessment from a Clinical Data Registry
Bibliographic record
Abstract
Introduction The Complex Chronic Diseases Program (CCDP) was funded by the BC Ministry of Health to address gaps in health services provision for Complex Chronic Diseases (CCDs). The Program offers medical interventions and education on self-management for people with Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), Fibromyalgia (FM), and Chronic Lyme-Like Syndrome (CLLS). The CCDP Data Registry was created in 2017 for monitoring participant outcomes and program evaluation. Methods This research outlined the CCDP model of care and analyzed patient-reported questionnaires and clinical data collected longitudinally from consented CCDP Data Registry participants spanning June 2017 through September 2022. T-tests and linear regression modelling were conducted to ascertain changes in symptom presentation across program involvement. These analyses specifically targeted data at the following time points: baseline, 6 months follow-up, and discharge. Results Data reported in this study represented 668 eligible participants from the 1-year Program. Demographically, the average age was 49 years old (SD=13), 90% were women (n=557), 54% were diagnosed with ME/CFS and FM (n=360), and 36% reported being on long term illness/disability (n=219). Between baseline and discharge, participants with ME/CFS and FM reported improvements in overall physical and mental health, but no significant improvement in other symptom domains such as sleep, fatigue, and pain. The duration of disease at baseline was only related to sleep quality. The previous, more individualized model of care showed better mental health outcomes at 6-months follow-up. Discussion This analysis showed that CCDP patients experienced relatively severe and persistent symptom presentations. Participants involved in the Program experienced some health benefits at discharge, but further research and interventions are needed to optimize health outcomes. The reliance on self-report of symptoms and the absence of a control group without intervention limit the significance of these findings. A Strategic Direction Plan was developed by the CCDP which emphasized improved training, decentralized services, fast-tracking of eligible individuals, and enhanced education for better patient outcomes.
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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.023 | 0.007 |
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".