Global Consensus From Clinicians Regarding Low Back Pain Outcome Indicators for Older Adults: Pairwise Wiki Survey Using Crowdsourcing
Bibliographic record
Abstract
BACKGROUND: Low back pain (LBP) is one of the most debilitating conditions among older adults. Unfortunately, existing LBP outcome questionnaires are not adapted for specific circumstances related to old age, which may make these measures less than ideal for evaluating LBP in older adults. OBJECTIVE: To explore the necessity of developing age-specific outcome measures, crowdsourcing was conducted to solicit opinions from clinicians globally. METHODS: Clinicians around the world voted and/or prioritized various LBP outcome indicators for older adults on a pairwise wiki survey website. Seven seed outcome indicators were posted for voting while respondents were encouraged to suggest new indicators for others to vote/prioritize. The website was promoted on the social media of various health care professional organizations. An established algorithm calculated the mean scores of all ideas. A score >50 points means that the idea has >50% probability of beating another randomly presented indicator. RESULTS: Within 42 days, 128 respondents from 6 continents cast 2466 votes and proposed 14 ideas. Indicators pertinent to improvements of physical functioning and age-related social functioning scored >50 while self-perceived reduction of LBP scored 32. CONCLUSIONS: This is the first crowdsourcing study to address LBP outcome indicators for older adults. The study noted that age-specific outcome indicators should be integrated into future LBP outcome measures for older adults. Future research should solicit opinions from older patients with LBP to develop age-specific back pain outcome measures that suit clinicians and patients alike.
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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 | 0.021 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".