Prevalence and Characteristics of Patients with a Diagnosis of Chronic Obstructive Pulmonary Disease Participating in Non-Pulmonary Rehabilitation Programmes: A Brief Report
Bibliographic record
Abstract
PURPOSE: To determine the prevalence of people with a diagnosis of chronic obstructive pulmonary disease (COPD) among those completing non-pulmonary rehabilitation (NPR) programmes and to describe their characteristics. METHODS: Electronic data of participants who completed an in-patient rehabilitation programme between July 1, 2010, and July 1, 2012 were retrospectively reviewed. Data extracted were month and year of birth, sex, height, weight, referral source, admission and discharge dates, programme admitted to, reason for admission, most responsible health condition, number of co-morbidities, referral agency on discharge, and Functional Independence Measure (FIM) scores on admission and discharge. RESULTS: The prevalence of COPD among participants who completed the NPR programmes was 7.5%. The most common reasons for admission were cardiac conditions (n=69, 20%), followed by post-unilateral hip replacement (n=40, 11%) and post-unilateral hip fracture (n=38, 11%). Patients were discharged after an average stay of 20 (SD 13) days. The mean FIM score was 91 (SD 11) at admission and 108 (SD 9) at discharge. CONCLUSIONS: The prevalence of a COPD diagnosis among participants in NPR programmes was 7.5%. COPD is a common comorbidity for people in rehabilitation programmes who have had amputations, have a cardiac condition, have undergone organ transplantation, or require complex care.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".