Development of the AOSpine Patient Reported Outcome Spine Trauma (AOSpine PROST): a universal disease-specific outcome instrument for individuals with traumatic spinal column injury
Bibliographic record
Abstract
PURPOSE: To report on the multi-phase process used in developing the AOSpine Patient Reported Outcome Spine Trauma (AOSpine PROST), as well as the results of its application in a pilot study. METHODS: The International Classification of Functioning, Disability and Health (ICF) methodology was used as the basis for the development of this tool. Four preparatory studies and a consensus conference were performed, and resulted in the selection of 25 core ICF categories as well as the scale for use. The first draft of the Dutch version of AOSpine PROST was pilot tested among a consecutively selected representative sample of 25 spine trauma patients, using the 'think aloud' and 'probing' methods. RESULTS: Of the 25 core ICF categories, 9 related to body functions, 14 activities and participation, and 2 environmental factors. Those 25 core categories were implemented into the selected response scale, and resulted in a draft version of AOSpine PROST consisting of 19 items. From the pilot study, very satisfactory results were obtained for comprehensibility, relevance, acceptability, feasibility and completeness, as well as high internal consistency (Cronbach's α = 0.926). CONCLUSIONS: Following the ICF methodology and including the results of 4 different preparatory studies and a consensus conference, the AOSpine PROST is developed. Taking the results from the subsequent pilot study into account, a definite version to be further validated will be developed. The AOSpine PROST has the potential to be a helpful tool in clinical practice and research to compare various treatments and improve the quality of health 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 | 0.010 | 0.025 |
| 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 | 0.002 |
| 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".