Referral of people with low back pain to physical therapists in Brazilian primary healthcare: A challenge revealed
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is a disabling condition worldwide, and current evidence suggests low rates of referral to physical therapists and extensive use of low-value interventions such as pharmacological treatments and emergency visits. OBJECTIVE: To investigate the frequency of referrals and characterize people with LBP accessing primary care physical therapists, as well as characterize clinical and sociodemographic aspects and the use of health resources in Brazil. METHOD: Observational study using nationwide data on 1,459,710 adults with LBP, stratified according to G1: medical care only, G2: medical care and referral to physical therapist, G3: physical therapist as first contact. Data were analyzed descriptively. RESULTS: 1,405,145 people with LBP were included in G1, followed by G2 (N:14,079), and G3 (N:40,486). The majority was female (56.3 %), and the average age was 49 (±17) years for females and 48 (±17) for males. Less than 1 % (G2) were referred to physical therapists. Of these, 8085 (57.4 %) had an average duration of 17.4 days (±65.6) between referral and their clinical appointment, and 5994 (42.6 %) had a longer duration (261.1 ± 146.9 days). A total of 130,570 (8.9 %) participants were referred for imaging, totaling 152,150 exams. G1 had 105.65 exams/1000 people and 128 referrals to specialists/1000 people. G2 had 196.32 exams and 384.76 referrals to specialists/1000 people, and G3 had 22.87 exams and 64.89 referrals to specialists/1000 people. CONCLUSION: We found a relatively low number of referrals of people with LBP to physical therapists in primary health care in Brazil, and a long period between the referral and the first contact with this professional. In addition, diagnostic imaging and referral to a specialist were frequently used.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".