Comparing Patient Reported Outcomes to Objective Measures of Function in patients with Chronic Pain: Using the SF-12, 2 minute walk test and Elevation and Movement Lift test
Bibliographic record
Abstract
Abstract Introduction An Ideal battery of testing for function would be inexpensive, easily administered, standardized and validated for multiple health issues. This would also be sensitive to change over time and able to extrapolate avocational and vocational tasks. The data collection exercise for this study included both subjective and objective measures which include the Short Form Health Survey 12 (SF-12), the 2 minute walk test (2MWT) and a newly developed upper extremity strength/conditioning activity called the Elevation and Movement Lift test (EMLi). Methods A convenience sample of 102 patients with chronic pain. They were seen in the Canadian Forces Health Services Unit (CFHSU (O)) Physiatry Clinic between January-September 2019 and were asked to complete the data intake protocol. This included: completing a questionnaire with the Numeric Rating Pain Scale (NRS) covering the previous 7 days, the SF-12, and completed the 2 MWT and EMLi. Results For the 2MWT heart rate, perceived exertion and number of steps were all increased for patients with chronic pain compared to the control group. There was no difference noted between patients with upper/lower body pain. In patients with chronic pain SF-12 physical function score negatively correlated with perceived exertion but not performance. As for the EMLI test, all groups had similar perceived exertion and heart rate outcomes but a reduced performance was noted with the upper extremity group. Discussion For the 2MWT, the individual’s performance related to effort and not their pain state, PE and SF-12. This suggests a higher capacity for walking then the patients realise. As for the EMLi, individual’s performance was poorer for same level of effort. This correlates to their perceived function as seen on the SF-12 which might measure pain related dysfunction. Conclusion The 2MWT performance was effort dependent and not correlated with perceived abilities. Therefore it can be used to challenge patient performance. EMLi performance correlated with perception and upper extremity pain. This could be used to set clinical training targets and monitor each individual’s progress.
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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 | 0.002 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".