Digital Pain Drawings Can Improve Doctors’ Understanding of Acute Pain Patients: Survey and Pain Drawing Analysis
Bibliographic record
Abstract
BACKGROUND: Pain drawings (PDs) are an important tool to evaluate, communicate, and objectify pain. In the past few years, there has been a shift toward tablet-based acquisition of PDs, and several studies have been conducted to test the usefulness, reliability, and repeatability of electronic PDs. However, to our knowledge, no study has investigated the potential role of electronic PDs in the clinical assessment and treatment of inpatients in acute pain situations. OBJECTIVE: The aim of this study was to evaluate whether knowledge of the patients' electronic PD has the potential to improve the doctors' understanding of their patients and to influence their clinical decision making. Furthermore, we sought to identify differences between electronic PDs of patients and their treating pain specialists in an acute pain situation and to find those specific characteristics derived from the PDs that had the largest impact on doctors' understanding. METHODS: We obtained electronic PDs from 47 inpatients in acute pain situations before their consultation with a pain specialist on a tablet personal computer with a stylus. Before looking at their patients' drawings, these specialists drew their own conception of the patients' pain after anamnesis and physical examination. Patients' drawings were then revealed to the doctors, and they were asked to evaluate how much the additional information improved their understanding of the case and how much it influenced their clinical decision on an 11-point Likert scale (0="not at all" and 10="very much"). Similarities and differences of patients' and doctors' PDs were assessed by visual inspection and by calculating Jaccard index and intraclass correlation coefficient (ICC) of the pain area and the number of pain clusters. Exploratory analyses were conducted by means of correlation tables to identify specific factors that influenced doctors' understanding. RESULTS: Patients' PDs significantly improved the doctors' understanding (mean score 4.81, SD 2.60, P<.001) and to a lesser extent their clinical decision (mean 2.68, SD 1.18, P<.001). Electronic PDs of patients and doctors showed fair to good similarity for pain extent (r=.454, P=.001) and widespreadness (P=.447, r=.002) were important factors helping doctors to understand their patients. CONCLUSIONS: In a clinical setting, electronic PDs can improve doctors' understanding of patients in acute pain situations. The ability of electronic PDs to visualize differences between doctors' and patients' conception of pain has the potential to improve doctor-patient communication.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".