Measuring Workload of Clinical Trials: Transcultural Adaptation and Validation to Portuguese Lanquage of Ontario Protocol Assessment Level (OPAL)
Bibliographic record
Abstract
Introduction:The increase number of clinical protocols with different requirements and specificities, the demand for the quality of the data according to the good clinical practices evidence the need of an instrument capable of measuring the workload of clinical protocols, and to assist the management of research centers.The object of study is the instrument entitled the Ontario Protocol Assessment Level, created for measuring the workload of the research coordinator, focusing on the complexity of clinical protocols in oncology.Aim: To perform a transcultural adaptation and validation of the instrument in terms of the Portuguese language.Method: This is a methodological research, whose chosen scenario was the clinical research center of the Brazilian National Cancer Institute, located in Rio de Janeiro.The subjects were the clinical research coordinators.The research was approved by the ethics committee, under protocol 120.006.Results: A significantly high degree of agreement between intra-and inter-observers was established; the agreement of the committee of specialists (the golden standard) was considered to be excellent (ICC>0.949) in both research periods (1 and 2); this score demonstrates a high level of validation.The analytical process confirmed that the tool score did not overestimate nor underestimate the evaluation of the committee of specialists. Conclusion:The instrument was considered valid and reliable based on the statistical tests performed.It provides the support required to calculate the workload generated by clinical protocols.
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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 | 0.120 | 0.224 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".