How to use the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) in orthodontic research
Bibliographic record
Abstract
INTRODUCTION: Clinical trial protocols are essential documents that serve as a basis for research planning. The Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) statement aimed to increase the transparency and integrity of clinical trial protocols. OBJECTIVES: This paper described the main aspects of the SPIRIT, highlighting the importance of using this guideline in Orthodontics. RESULTS: The SPIRIT is composed of 33 items and the diagram, which were presented and explained. CONCLUSION: The use of the SPIRIT checklist must become essential to increase the transparency and integrity of more reliable and less biased clinical trials in orthodontic research, improving the quality of future publications in this field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | Metaresearch Domain: Reporting · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Metaresearch Domain: Reporting · Genre: Methods 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 | 0.565 | 0.765 |
| Meta-epidemiology (narrow) | 0.005 | 0.008 |
| Meta-epidemiology (broad) | 0.010 | 0.018 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.009 | 0.008 |
| Research integrity | 0.024 | 0.033 |
| Insufficient payload (model declined to judge) | 0.015 | 0.013 |
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".