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Record W2151502817 · doi:10.4103/0970-1591.44245

What every urologist should know about surgical trials Part II: What are the results and should I apply them to patient care?

2008· article· en· W2151502817 on OpenAlex
Philipp Dahm, Sohail Bajammal, Mohit Bhandari

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIndian Journal of Urology · 2008
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsMcMaster UniversityUniversity of Calgary
Fundersnot available
KeywordsMedicineGeneral surgeryMedical physics

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.030
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.444
GPT teacher head0.493
Teacher spread0.049 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it