Bibliographic record
Abstract
ho does not want to leave a legacy?It is a basic human desire, maybe even a need, to leave something on this earth that in some way marks that we were here, albeit for a blink in time.In life we do this by what we build and the children we create and nurture.In the medical world we can create a legacy in several ways: by teaching, by patient care and by research.Of these 3 categories, research lends itself to perhaps the most objective measure by the application of simple mathematics.In this issue of CUAJ, Hennessey and colleagues 1 present their results in the search for "The top 100 cited articles in urology."Within the limitations of the methodology, these cited articles provide a history of the important milestones of our specialty from 1965 to the present.The start point of 1965 reflects the limit of the databases, which therefore leaves out some pivotal articles that form the basis of many subsequent breakthroughs: Halifax-born Nobel laureate Charles Huggins and his description of the relationship between the hormonal milieu and the progression of prostate cancer is an example, since his seminal work was published before 1965 (he was awarded the Nobel Prize in Medicine in 1966).Another limitation of the methodology is the anglophone dominance of the journals that were searched: 92% of the 93 journals are published in the English language.Although, as a unilingual anglophone, this ratio does reflect my reading preference, I am not confident that it truly reflects the worldwide experience.For instance, is it possible that Chaussy and colleagues' 1982 paper describing the use of extracorporeal shock wave lithotripsy (number 92) was also published in their native German language?It is also apparent that certain subspecialties of urology are underrepresented, possibly because of the decisions by the authors concerning which journals to include.The complete absence of articles related to pediatric urology can be explained by this bias.We also must not confuse popularity with importance.
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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.053 | 0.022 |
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".