Bibliographic record
Abstract
O rgan and colleagues evaluate 207 small renal masses in 169 patients in a 5-year multi-institutional follow-up study to assess tumour growth kinetics.1 After a median follow-up of 603 days and a median of 5 radiological studies, the median growth rate of the entire cohort was 0.12 cm/year.An analysis of the cohort using recursive partitioning analysis algorithm did not identify any variables, such as age, symptoms, tumour consistency and maximum diameter at diagnosis that were predictive of growth.In a previous study involving this dataset, the authors demonstrated that growth rate was also not affected by biopsy proof of malignancy and that progression to metastasis occurred in only 1.1% of patients.2 This current study has its limitations, including radiological follow-up using different imaging modalities (computed tomography, magnetic resonance imaging, ultrasonography), which can provide subtle measurement differences when compared, a lack of central radiological review, the use of maximum diameter instead of tumour volume and the absence of precise tumour location (exophytic vs. endophytic or sinus based).Yet, despite these limitations, an important message can be gleaned.Basic clinical factors do not correlate with or predict subsequent tumour growth.Twenty years ago, all renal tumours, regardless of size, patient ages, and overall medical condition, were treated with radical nephrectomy provided there was a normal appearing contra lateral kidney.However, our current approach to these tumours, particularly the smaller ones, is rapidly changing.Now we understand that renal cortical tumours have complex biology and variable metastatic potential, with nearly 50% being benign neoplasms or indo-
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".