Type II Error in the Spine Surgical Literature
Bibliographic record
Abstract
In Brief Study Design. A literature review. Objectives. To determine the frequency of potential type II errors published in the spine surgical literature. Summary of Background Data. The randomized controlled trial is the strongest clinical evidence available in investigational medicine. Unfortunately, it is common for randomized controlled trials published in peer-reviewed journals not to report a primary question or a sample size calculation. When the null hypothesis is accepted and the power of a study is unreported, the validity of a study’s findings may be significantly limited. To our knowledge, the spine literature has not been appraised to determine the frequency of type II errors. Methods. A literature search was conducted of MED-LINE, PubMed, and Cochrane databases, using the key words of “spine” and “surgery” between 1967 and 2002. Trials were included if they were of a 2-group randomized controlled trial design, which reported a nonsignificant difference in the primary outcome. The frequency of reporting the primary outcome and sample size calculation was determined. The sample size was assessed to determine whether the trial had sufficient patients to detect a 10%, 25%, and 35% relative difference in the primary outcome for a power of 80%. Results. A total of 37 studies satisfied the inclusion criteria. Six studies reported a sample size calculation (17%). Of the remaining 31 studies, 5 explicitly stated a primary outcome (14%). The mean type II error (beta error) was 82%. Conclusion. The spine surgical literature is plagued with a high potential for type II error. A trial’s methodology should be scrutinized to prevent misinterpretation of the results. A review of the spine surgical literature revealed 37 randomized controlled trials that met the inclusion criteria. Seventeen percent of these articles calculated a sample size a priori. The remaining 31 trials had an average power of 18%. Only 17% of these studies were appropriately powered to detect a clinically significant difference between treatment groups.
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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 | 0.486 | 0.793 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.033 | 0.023 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".