Patients’ Recall of Diagnostic and Treatment Information Improves with Use of the Pain Explanation and Treatment Diagram in an Outpatient Chronic Pain Clinic
Bibliographic record
Abstract
Educating patients about their condition and treatment plan is an essential component of successful treatment. Patients need to understand their condition, recall treatment suggestions and comply with the treatment protocol. Unfortunately, the method of patient education most commonly used by physicians is verbal teaching and discussion, which leads to poor patient recall. The authors of this article developed the Pain Explanation and Treatment Diagram, a worksheet that the clinician completes with the patient during their first consultation to function as a record of their diagnosis, pain risk factors and an action plan for when pain occurs. In this study, patient recall, among other variables, was assessed as a function of time since first appointment. BACKGROUND: To maximize the benefit of therapies, patients must understand their condition, recall treatment suggestions and comply with treatments. The Pain Explanation and Treatment Diagram (PETD) is a one‐page worksheet that identifies risk factors (health‐related habits, sleep, exercise, ergonomics and psychosocial factors) involved in chronic pain. Clinician and patient complete the PETD together, and the clinician notes recommended treatments and lifestyle changes. OBJECTIVES: To examine the effect of use of the PETD on patient recall of diagnostic and treatment information on the sheet. METHODS: The present study was a cross‐sectional analysis. Patients with chronic musculoskeletal pain seen by one physiatrist at an outpatient pain clinic in a university‐affiliated hospital between 2009 and 2012 (all of whom received the PETD) were eligible. A structured telephone interview lasting approximately 1 h was used to determine recall of PETD diagnostic and treatment information. RESULTS: Of the 84 eligible patients, 46 were contacted and 29 completed the telephone interview. Participants recalled an average of 12.2% (95% CI 7.8% to 17.4%) of items without prompting and 48.5% (95% CI 42.0% to 53.5%) when prompted. Participants who referred to the PETD at home (n=13) recalled significantly more items than those who did not (n=15) (17.6% [95% CI 11.1% to 23.9%] versus 5.2% [95% CI 3.0% to 14.5%], P=0.004); when prompted, the rates increased to 54.3% (95% CI 48.3% to 61.2%) and 41.2% (95% CI 34.7% to 50.7%), respectively (P=0.032). CONCLUSIONS: The PETD is a promising, feasible and inexpensive tool that can improve patients’ recall of diagnostic‐ and treatment‐related information.
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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 | 0.005 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".