Physician‐to‐Physician Telephone Consultations for Chronic Pain Patients: A Pragmatic Randomized Trial
Bibliographic record
Abstract
BACKGROUND: The impact of telephone consultations between pain specialists and primary care physicians regarding the care of patients with chronic pain is unknown. OBJECTIVES: To evaluate the impact of telephone consultations between pain specialists and primary care physicians regarding the care of patients with chronic pain. METHODS: Patients referred to an interdisciplinary chronic pain service were randomly assigned to either receive usual care by the primary care physician, or to have their case discussed in a telephone consultation between a pain specialist and the referring primary care physician. Patients completed a numerical rating scale for pain, the Pain Disability Index and the Short Form-36 on referral, as well as three and six months later. Primary care physicians completed a brief survey to assess their impressions of the telephone consultation. RESULTS: Eighty patients were randomly assigned to either the usual care group or the standard telephone consultation group, and 67 completed the study protocol. Patients were comparable on baseline pain and demographic characteristics. No differences were found between the groups at six months after referral in regard to pain, disability or quality of life measures. Eighty percent of primary care physicians indicated that they learned new patient care strategies from the telephone consultation, and 97% reported that the consultation answered their questions and helped in the care of their patient. DISCUSSION: Most primary care physicians reported that a telephone consultation with a pain specialist answered their questions, improved their patients' care and resulted in new learning. Differences in patient status compared with a usual care control group were not detectable at six-month follow-up. CONCLUSIONS: While telephone consultations are clearly an acceptable strategy for knowledge translation, additional strategies may be required to actually impact patient outcomes.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".