Evaluating quality of care for patients with rotator cuff disorders
Bibliographic record
Abstract
BACKGROUND: Measuring quality in healthcare is vital in evaluating patient outcomes and system performance. The availability of reliable and valid information about the quality of care for patients presenting with rotator cuff disorders (RCD) in Alberta, Canada is scarce. The objective of this study is to measure quality of care for patients with RCD in order to identify areas of improvement. METHODS: This study employs descriptive survey research design. Between March 2015 and November 2016, a convenience sample of patients presenting with chronic, full-thickness rotator cuff tears to two sport medicine centres in Calgary and Edmonton, Alberta completed two questionnaires: the Healthcare Access and Patient Satisfaction Questionnaire (HAPSQ) and the Rotator Cuff Quality-of-Life Index (RC-QOL). Data collected using both questionnaires were used to make judgments about quality of care. Quality of care was evaluated using six dimensions of quality defined by the Alberta Quality Matrix for Health: accessibility, acceptability, efficiency, effectiveness, appropriateness, and safety. Data was also used to compare current patient clinical pathways to ideal clinical pathway algorithms and used to make judgments about the appropriateness and safety of healthcare practices. RESULTS: One hundred seventy-one patients participated in the study. The longest mean waiting times for medical services in Alberta were for magnetic resonance imaging (MRI) received in the public sector (103 days) and consultation by orthopaedic surgeon (172 days). Patient satisfaction with respect to quality of care was lowest for emergency room physician and highest for orthopaedic surgeon visits. Patients were treated by a mean of 2.5 physicians (SD: 0.77, range: 2-7). The total aggregate average cost per patient was $4541.19. The mean RC-QOL score for all patients was 42 (SD: 22). Only 54 patients (64%) requiring surgery were able to consult with a surgeon within benchmark timeframes. A comparison of current to ideal clinical pathway algorithms found that 38 patients (22%) experienced indirect clinical pathways, whereby care was fragmented and patients received care from multiple and often, redundant healthcare professionals. CONCLUSION: There is a discrepancy between current and ideal clinical pathways whereby some patients are experiencing quality of care that is inefficient, disjointed, and less than ideal.
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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".