Changes in primary care physician’s management of low back pain in a model of interprofessional collaborative care: an uncontrolled before-after study
Bibliographic record
Abstract
BACKGROUND: Tracking how clinicians treat patients provides an opportunity to explore how the clinical management of common musculoskeletal disorders evolves over time. We present an uncontrolled before-after study of a primary care physician's management of low back pain and describe how his involvement in an interprofessional collaborative practice was associated with a change in the management of patients with low back pain. METHOD: Data from the electronic medical record of one primary care physician who participated in a study of a model of chiropractic-medical collaboration were retrospectively collected. Records of a sample of consecutive patients prior to the start (i.e. pre-study, n = 51) and at the end of the collaborative study (i.e. study, n = 49) were collected. RESULTS: Demographics were similar in both groups but median number of physician visits (2.5 and 1.0), average prescriptions per patients (1.24 and 0.47), and total number of narcotic prescriptions (14 and 6) differed between pre-study and study groups, respectively. Separate analysis of only the records of low back pain study patients revealed that 61% were referred for chiropractic care during the study period. Patients who were not referred had more neurological deficits and leg pain but back pain severity and average number of prescriptions was about the same. Referred patients in the study group had about 25% fewer physician visits and imaging requests. CONCLUSION: Based on this study of a single primary care physician, we hypothesize that doctors may change their prescribing behaviours and consultation rate for patients with low back pain when engaged in interprofessional collaborative care. Further research is required to test this observation in the population.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".