Painful Memories: Reliability of Pain Intensity Recall at 3 Months in Senior Patients
Bibliographic record
Abstract
Background.Validity of pain recall is questioned in research.Objective.To evaluate the reliability of pain intensity recall for seniors in an emergency department (ED).Methods.This study was part of a prospective multicenter project for seniors (≥65 years old) treated in an ED for minor traumatic injury. Pain intensity (0–10 numerical rating scale) was evaluated at the initial ED visit, at one week (baseline), and 3 months. At three months, patients were asked to recall the pain intensity they had at baseline.Results.482 patients were interviewed (mean age 76.6 years, SD ± 7.3) and 72.8% were female. Intraclass correlation coefficient between pain at baseline and its recall was 0.24 (95% CI: 0.14–0.33). Senior patients tended to overestimate their pain intensity by a mean of 1.2 (95% CI: 0.9–1.5) units. A stepwise multiple regression analysis showed that the variance of baseline pain recall at 3 months was explained by pain at ED visit (11%), pain at 3 months (7%), and pain at baseline (2%).Conclusion.The accuracy of pain intensity recall after three months is poor in seniors and seems to be influenced by the pain experienced at the time of injury.
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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".