Prostatic mapping in diagnosis and follow-up for prostatic cancer patients treated by CT-RT
Bibliographic record
Abstract
This method permits to demonstrate a positive diagnosis for cancer in 27 patients (60%); other patients underwent to another prostatic mapping after an year because of high PSA level (18 prostatic mapping, just a case positive for cancer). Other three follow-up patients, demonstrated a high PSA level higher than 0.5 ng/ml. Prostatic mapping (12 biopsy) allowed us to confirm a recurrence neoplasm. This reduction in the PCa mortality rate, which has coincided with the widespread use of the PSA test, is to be attributed to the higher number of organ-confined (and therefore curable) cancers detected but also to a more effective treatment of the advanced disease and to a more accurate identification of causes of death; in fact, the areas with the highest rates of early detection and treatment (USA and Canada) do not report the lowest mortality rates. The reduction in PCa mortality in Italy has been noted in all the age groups where there had been the highest increase in the incidence of this cancer. Early diagnosis of an increasing number of organ-confined cases and the resulting intention-to-treat approach are likely to have contributed to this outcome. Early diagnosis, screening for this kind of cancer (PSA dosage and prostatic mapping), natural biological behavior and evolution in medical and surgical treatment had improved prognosis, life quality for patient who had diagnosed prostatic cancer.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".