Considerations when designing, analyzing, and reporting reliability studies
Bibliographic record
Abstract
BACKGROUND: Reliability studies have a long history in the physical therapy literature and their sophistication has evolved over the decades. Often, however, there has been incomplete reporting or a lack of coherence among study purpose, design, choice of analysis, sample size justification, and reporting of results. Two possible explanations for this oversight are a vaguely written purpose statement and statistical software that does not provide all essential information. OBJECTIVE: The goal of this masterclass is to provide considerations and resources to assist investigators structure a coherent reliability study design and subsequent presentation of results. DISCUSSION: This masterclass highlights the importance of framing a study purpose that clearly distinguishes between a hypothesis testing and parameter estimation study and appropriately labelling the study design. It also stresses the importance of stating whether the raters are the only ones of interest or whether they are intended to represent a larger group of raters, applying a sample size calculation consistent with the study purpose, and reporting results that align with the study purpose and design.
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
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 | 0.604 | 0.843 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".