Latent classes of trial reporting and publication practices in spinal manipulation research: a meta-epidemiological study
Bibliographic record
Abstract
Reliable reporting and publication practices are essential for trustworthy evidence synthesis and clinical decision-making. We aimed to identify latent classes of randomized controlled trials (RCTs) evaluating spinal manipulative therapy (SMT) based on trial reporting and publication practices, and to examine whether these classes influenced treatment effects. Meta-epidemiological study. Trials were evaluated on whether they met criteria for trial reporting and publication practices across six domains. Latent class analysis was used to identify trial subgroups. Random-effects meta-regression models assessed whether class membership predicted pooled estimates of treatment effects for pain and disability. We included 239 RCTs and identified four classes: Dated (23%), older trials (mostly pre-2010) with consistently low proportions of criteria met; Non-contributing (30%), newer trials that inconsistently met the criteria, had small samples, and short follow-ups; SMT-focused (15%), which reported SMT details and fidelity more consistently but otherwise resembled the Non-contributing class; and Pragmatic (33%), consisting of larger trials, meeting most criteria, but often underreported SMT-specific and fidelity details. Reporting practices had larger impact on class membership than publication practices. Despite differences class membership was not associated with treatment effect estimates and explained minimal outcome variability (R 2 ∼1%). Although trial reporting and publication practices varied substantially across SMT trials, these differences were not associated with differences in treatment effects. The widespread failure to meet key criteria raises concerns about the interpretability and credibility of the SMT evidence base. To strengthen transparency and scientific value, future trials should adhere more rigorously to reporting guidelines. • Four latent trial classes identified Dated, Non-contributing, SMT-focused, Pragmatic • Class membership did not explain pain or disability outcome differences • Reporting deficiencies persist in SMT trials, including many recent studies • Improved adherence to reporting guidelines is urgently needed
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | MetaresearchMeta-epidemiology (broad) Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | MetaresearchMeta-epidemiology (broad) Domain: Reporting · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.686 | 0.834 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.027 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.014 | 0.013 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".