A physical therapist and nurse practitioner model of care for chronic back pain using telehealth: Diagnostic and management concordance
Bibliographic record
Abstract
Introduction Virtual care using videoconference links between urban-based physical therapists and nurse practitioners in rural primary care may overcome access challenges and enhance care for rural and remote residents with chronic low back disorders (CBD). The purpose of this study was to evaluate the concordance of this new model of care with two traditional models. Methods In this cross-sectional study design, each of 27 participants with CBD were assessed by: 1) a team of a nurse practitioner (NP) located with a patient, joined by a physical therapist (PT) using videoconferencing (NP/PT team ); 2) in-person PT (PT alone ); and 3) in-person NP (NP alone ). Diagnostic and management concordance between the three groups were assessed with percent agreement and kappa. Results Overall diagnostic categorization was compared for PT alone versus NP alone and NP/PT team : percent agreement was 77.8% ( k = 0.474, p = 0.001) and 74.1% ( k = 0.359, p = 0.004), respectively. In terms of management recommendations, the PT alone and NP alone demonstrated strong agreement on “need for urgent surgical referral” (92.6%, k = 0.649 ( p < 0.00) and slight agreement for “refer to primary physician for pharmacology, lab or imaging” (81.5%, k = 0.372 ( p = 0.013). The PT alone and NP/PT team demonstrated strong agreement on “need for urgent surgical referral” (96.3%, k = 0.649, p = 0.000) and “recommendation for PT follow up” (88.9%, k = 0.664, p = 0.000). Discussion The diagnostic categorization and management recommendations of the team using videoconferencing for CBD were similar to decisions made by an in-person PT. This model of care may provide a method for enhancing access to PT for CBD assessment and initial management in underserved areas.
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 | 0.032 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".