Bibliographic record
Abstract
very urologist wants to be able to tell their patient they had negative margins after a prostatectomy, and every patient wants to hear it. However, as a quality of care indicator, surgical margin status represents a significant dilemma. The variation in rate of margin positivity in different surgical series is remarkable, most obviously if it reflects surgeon experience from a centre of excellence or from a population-based study. 1 Furthermore, using margin status as an indicator of quality is confusing as it is often as much a reflection of case mix or pathological expertise as it is surgical technique. Most of us would accept that a positive margin has a negative impact on disease outcomes after radical prostatectomy. The strength of its prognostic value, however, may vary depending on postoperative risk status and is uncommonly demonstrated to be predictive of clinical progression, cancer-specific or overall mortality. Still, the psychological burden of a positive margin should assure our individual commitment to improving this outcome.
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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.017 |
| Insufficient payload (model declined to judge) | 0.157 | 0.091 |
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".