Reliability and validity of the Finnish version of the AO spine PROST (patient reported outcome spine trauma)
Bibliographic record
Abstract
PURPOSE: To translate AO Spine PROST (Patient Reported Outcome Spine Trauma) into Finnish and explore its psychometric properties in a Finnish spine trauma population. METHODS: Patients were enrolled at an academic level-I trauma center. Score distribution, floor and ceiling effects, and missing items were explored for content validity. Internal consistency, concurrent validity, and reproducibility were explored with Cronbach's α, exploratory factor analysis, Spearman correlation tests, and Intraclass Correlation Coefficients (ICCs). RESULTS: Translation was performed using established guidelines. A sample of 110 patients was enrolled. Score distribution skewed toward higher values, but no floor and ceiling effects were present. Response rates were excellent for all items except for Work/study (60%). Factor analysis indicated two possible dimensions in PROST, explaining 75.3% of the total variance. For PROST total score, correlations were strong with EQ-5D-3 L (r = 0.77 [95% confidence interval: 0.69-0.84]) and ODI (r=-0.89 [0.92-0.84]). Test-retest reliability was excellent with ICC = 0.86 (0.76-0.91). Calculation of the PROST score without the Work/study item appeared valid (ICC 0.99 [0.98 to 1.00]). CONCLUSIONS: The Finnish version of AO Spine PROST is a reliable and valid measure for spine trauma outcomes. We recommend it for clinical practice and research to reduce the current controversies in spine trauma care.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".