Historical controls in orthodontics: need for larger meta-epidemiological studies
Bibliographic record
Abstract
Dear Sir, In their meta-epidemiological study, Papageorgiou et al. (1) report on the impact of the design of untreated control groups on the magnitude of treatment effect size estimates in orthodontic trials. While the authors investigate an important research question with potential clinical relevance, there are some points that need to be considered when interpreting their results. First, the study in question included 122 trials involving different orthodontic interventions with large numbers of study outcomes; this number of trials could be insufficient to demonstrate a meaningful difference in the magnitude of the treatment effect size estimate (2, 3). By having a larger number of meta-analyses and trials, the power and precision of an analysis can be increased, heterogeneity can be minimized, and generalizability of the results can be assured (2). Second, the current study included both randomized and nonrandomized (prospective and retrospective) trials; this procedure likely increases the confounding factors associated with meta-epidemiological studies and affects the calculated difference in magnitude of the treatment effect size estimate (4). Third, a robust meta-meta-analytic analysis requires the inclusion of meta-analyses that include a minimum of five randomized trials that provide quantitative data for the treatment effect size estimate, using the meta-analysis as the level of analysis (5). The current study did not take this issue into consideration. Although this study is welcome, as it sheds light on the ongoing discussion about the pertinence of using orthodontic historical controls, it could be considered heterogeneous and underpowered. Also, the authors’ conclusions may not be highly robust, and a meta-epidemiological study with a larger number of trials is needed to confirm or refute the findings of this timely report. In any event, the authors had to deal with the available literature, and an improved meta-epidemiological study in this area cannot be expected to be conducted in the near future due to the small number of published orthodontic trials (6). The intention here is not to suggest that the published effort is questionable but that the findings of this study may be biased due to the limited high-quality data available in orthodontics. None declared. HS is supported through a Clinician Fellowship Award by Alberta Innovates–Health Solutions (AIHS), the Honorary Izaak Walton Killam Memorial Award by the University of Alberta and the Honorary WCHRI Award by the Women and Children’s Health Research Institute (WCHRI).
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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 | Meta-epidemiology (broad) Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | MetaresearchMeta-epidemiology (narrow)Meta-epidemiology (broad) Domain: Methods · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Not applicable | 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 | 0.316 | 0.628 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.014 | 0.013 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.017 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.016 | 0.023 |
| Insufficient payload (model declined to judge) | 0.010 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".