From Paper to Digitalized Body Map: A Reliability Study of the Pain Area
Bibliographic record
Abstract
BACKGROUND: Computerized methods to analyze pain drawings (PDs) have been developed and may aid to measure the pain area more precisely. OBJECTIVE: The aim of this study was to verify whether examiners can reproduce the patient's PDs with acceptable reliability. METHODS: This was an intra-rater and inter-rater reliability study. The protocol consisted of 4 steps: (1) scanning of paper PDs; (2) sharing the digitalized PD images between examiners; (3) reproducing the PD images in the sketching application; and (4) calculating the pain area in pixels and percentages. We calculated intraclass correlation coefficients (ICCs; 2,1), the standard error of the measurement (SEM), and the smallest detectable difference (SDD). RESULTS: Reliability was tested using 31 PDs from 17 patients in our database (11 female [64.7%], mean age: 53.23 ± 11.57 years). Intra-rater reliability varied from ICC (2,1) = 0.991 (95% confidence interval [CI] = 0.982 to 0.996; SEM = 3,432.45; SDD = 162.39 pixels; P < 0.001) to ICC (2,1) = 0.992 (95% CI = 0.978 to 0.997; SEM = 3,412.96; SDD = 161.93 pixels; P < 0.001). Inter-rater reliability for the measurement between all examiners was considered excellent (ICC [2,1] = 0.976; 95% CI = 0.956 to 0.987; SEM =8,580.75; SDD = 256.76 pixels; P < 0.001), being higher between Examiners A and C (ICC [2,1] = 0.970; 95% CI = 0.936 to 0.986; SEM = 6,453.34; SDD = 222.67 pixels; P < 0.001). CONCLUSION: Our results show that intra- and inter-rater reliabilities were excellent when an examiner reproduced the paper PDs into digitalized PDs. This process gives clinicians and researchers the opportunity to analyze pain extent more precisely using a computerized method.
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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".