Bibliographic record
Abstract
The study by Lavallee and colleagues is a further attempt to garner data that may be fairly easily collected and collated and potentially used in the prognosis of men with prostate cancer having a radical prostatectomy.1 Such tools are appealing, but the likelihood of a drastic change from prostate-specific antigen (PSA), Gleason score and stage is difficult to ignore. This study explores tumour density and found it to be an independent predictor for biochemical recurrence. However, despite its worthy endeavour, tumour density adds little to nomograms. One cannot overstate Kattan’s publication in 1998 of the first nomogram in urologic oncology to predict radical prostatectomy recurrence using preoperative parameters.2 Today, one may wonder if we are attempting to “squeeze an already squeezed lemon dry” in attempting to improve nomograms. Almost all permutations and combinations of grade, PSA and pathological characteristics have been crunched to gain a small advantage, yet, in general, with minimal clinical consequence. It is doubtful that tumour density will become a common utility. Nomograms are becoming tiresome, although necessary. Catto summarizes our lust, hatred and fatigue for the tool we love to study in urology.3 The fact we persist with such tools acknowledges the complexities of treatment decision-making in many cases. One cannot argue with the authors that tumour volume measurement in prostate cancer as a stand-alone tool is unclear.1 Yet methods for calculating tumour volume are variable from the “eye of the pathologist” to more sophisticated digital methods. The use of volumetric analysis is now well-described.4 This digital type analysis was not universally done in this study.1 Furthermore, illustrating tumour volume by a tumour map may be helpful to understand the location of the tumour and also the need for surgery and adjuvant treatment. Certainly, there are limitations particularly that the methodology for calculating volume was not standardized nor was their independent review; there was also limited follow-up and patients were not contiguous.1 It is arguable that these may not have affected the final results, but they certainly detracted from their generalizability. Should we pursue tumour volume and related entities or continue our quest for other biomarkers and genetic profiling? They are not mutually exclusive and they may marry at a later date.5 For now, we will await further incremental advances in our quest for the “perfect” tool. Until we know all about prostate cancer, we should continue to collect data on tumour volume and even density, as markers inevitably will be discovered to complement such data and make for more relevant gains in our understanding of the prognosis of this common, but rarely lethal tumour.
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 | 0.048 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".