[Chronic musculoskeletal conditions and comorbidities in primary care settings].
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of chronic musculoskeletal conditions in primary care. Among patients with these conditions, to estimate the mean number of comorbidities and the prevalence of chronic diseases that could deteriorate with use of nonsteroidal anti-inflammatory drugs (NSAIDs). DESIGN: Secondary analysis of data collected for a study on the prevalence of multimorbidity. SETTING: Twenty-one family medicine practices in the region of Saguenay, Que. PARTICIPANTS: Two-tier sample consisting of family physicians (first tier) and their patients (second tier) recruited during consecutive consultation periods. MAIN OUTCOME MEASURES: Percentage of patients with chronic musculoskeletal conditions. Within this sub-sample, average number of comorbidities and percentage of patients with chronic diseases, such as hypertension, cardiovascular disease, renal disease, and stomach ulcers or reflux, that could deteriorate with use of NSAIDs. RESULTS: Among the 980 patients in the database, 58% had chronic musculoskeletal conditions. Average age of patients was 56 years. Among patients with these conditions,the number of comorbidities ranged from 0 to 11; the average number was 4. About 49% of patients had hypertension; 31% had cardiovascular disease; 31% had urinary problems or renal disease; and 17% had stomach ulcers or reflux. About 70% of patients with chronic musculoskeletal conditions had at least 1 of the 4 comorbidities mentioned. CONCLUSION: More than half the patients who consult in primary care have chronic musculoskeletal conditions. The average number of comorbidities these patients have is high; many present with comorbidities that can deteriorate with use of NSAIDs. Family physicians must, therefore, exercise caution when using NSAIDs for patients with musculoskeletal conditions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".