Lack of Prognostic Model Validation in Low Back Pain Prediction Studies
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to investigate the frequency with which prediction studies for low back pain outcomes utilize prospective methods of prognostic model validation. METHOD: Searches of Medline and Embase for terms "predict/predictor," "prognosis," or "prognostic factor." The search was limited to studies conducted in humans and reported in the English language. Included articles were all those published in 2 Spine specialty journals (Spine and The Spine Journal) over a 13-month period, January 2013 to January 2014. Conference papers, reviews, and letters were excluded. The initial screen identified 55 potential studies (44 in Spine, 11 in The Spine Journal); 34 were excluded because they were not primary data collection prediction studies; 23 were not prediction studies and 11 were review articles. This left 21 prognosis papers for review, 19 in Spine, 2 in The Spine Journal. RESULTS: None of the 21 studies provided validation for the predictors that they documented (neither internal or external validation). On the basis of the study designs and lack of validation, only 2 studies used the correct terminology for describing associations/relationships between independent and dependent variables. DISCUSSION: Unless researchers and clinicians consider sophisticated and rigorous methods of statistical/external validity for prediction/prognostic findings they will make incorrect assumptions and draw invalid conclusions regarding treatment effects and outcomes. Without proper validation methods, studies that claim to present prediction models actually describe only traits or characteristics of the studied sample.
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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 | 0.368 | 0.683 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".