Bibliographic record
Abstract
PURPOSE OF REVIEW: This review focuses on studies that help elucidate the optimum approach to posttreatment follow-up of breast cancer patients. RECENT FINDINGS: The re-conceptualization of follow-up under the rubric of survivorship care and the benefit of survivorship care plans, studies on the elements of follow-up care including surveillance mammograms, alternative models of follow-up care including primary care based follow-up, and patterns of care studies that involve population-based samples are discussed. Posttreatment follow-up of breast cancer patients continues to be controversial despite almost two decades of research. The research does show that surveillance mammograms are beneficial and guidelines recommend routine surveillance mammograms annually. Other routine surveillance tests are not beneficial and are not recommended. The precise frequency and duration of clinical visits is not known and recommendations vary; but most do support continued clinical assessment. Alternative models, such as nurse-led follow-up or less frequent follow-up, show good patient satisfaction and quality-of-life outcomes. Primary care based followup results in similar clinical and quality-of-life outcomes as specialist-based follow-up. SUMMARY: Instead of trying to find a one-size-fits-all approach, the focus should be on an individualized tailored approach in which the patient makes an informed decision on the basis of evidence of actual benefits and risks.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".