What every urologist should know about surgical trials Part II: What are the results and should I apply them to patient care?
Bibliographic record
Abstract
UNLABELLED: Surgical interventions have inherent benefits and associated risks. Before implementing a new therapy, we should ascertain the benefits and risks of the therapy, and assure ourselves that the resources consumed in the intervention will not be exorbitant. MATERIALS AND METHODS: We suggest a three-step approach to the critical appraisal of a clinical research study that addresses a question of therapy. Readers should ask themselves the three following questions: Are the study results valid, what are the results and can I apply them to the care of an individual patient. This second review article on surgical trials will address the questions of how to interpret the results and whether to apply them to patient care. RESULTS: Once a study has been determined to be valid, one should determine how effective an intervention is using either relative (i.e. risk ratio, relative risk reduction) or absolute measures (i.e. absolute risk reduction, number-needed to treat) of effect size. The reader should then determine the range within which the true treatment effect lies (95% confidence intervals). Having found the results to be of a magnitude that is clinically relevant, one must then consider if the result can be generalized to one's own patient, and whether the investigators have provided information about all clinically important outcomes. Then, it is necessary to compare the relative benefits of the intervention with its risks. If one perceives the benefits to outweigh the risks, then the intervention may be of use to one's patient. CONCLUSION: Given the time constraints of a busy urological practice, applying this three-tiered approach to every article will be challenging. However, knowledge of the critical steps to assess the validity, impact and applicability of study results can provide important guidance to clinical decision-making and ultimately result in a more evidence-based practice of urology.
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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 | 0.176 | 0.540 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.030 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".