Knowledge about urology in the general population: alarming results
Bibliographic record
Abstract
Introduction: For different reasons, urology appears to be one ofthe least known specialties in medicine. In this study, the mainobjective was to measure the knowledge about urology in thegeneral population.Methods: A questionnaire was completed by 150 respondents.Seventy-five respondents were from Sherbrooke, QC (a Frenchspeakingcity) and 75 were from Burlington, VT (an Englishspeakingcity).Results: Of the 150 respondents, 75% (113) said they knew littleor nothing about urology and 63% (95) did not know that urologyinvolves surgery. Also, when asked to name 3 diseases withinthe field of urology, 59% (89) of the respondents were unable toname at least 1 disease.Conclusion: This study demonstrates the lack of knowledge abouturology in the general population. As urologists, we have to considerthe need to conduct an informational campaign to increasethe awareness of urology and the services urologists provide.Introduction : Pour toutes sortes de raisons, l’urologie semble êtreune des spécialités les moins connues de la médecine. Notre étudeavait pour principal objectif de vérifier les connaissances de lapopulation générale en ce domaine.Méthodologie : Un questionnaire a été rempli par 150 personnes,réparties comme suit : 75 personnes de Sherbrooke, au Québec(ville francophone), et 75, de Burlington, au Vermont (ville anglophone).Résultats : Sur les 150 répondants, 75 % (113) avouaient avoirdes connaissances faibles ou nulles dans le domaine de l’urologieet 63 % (95) ignoraient que la chirurgie peut parfois être uti -lisée comme traitement urologique. En outre, lorsqu’on a demandéaux répondants de nommer 3 maladies traitées par un urologue,59 % (89) des répondants étaient incapables de nommer seulementune maladie.Conclusion : L’étude illustre le manque de connaissances de lapopulation générale dans le domaine de l’urologie. En tant qu’urologues,nous devons reconnaître la nécessité de mener une campagned’information afin de sensibiliser la population au sujet del’urologie et des services que nous offrons.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".