A prospective surveillance model for physical rehabilitation of women with breast cancer
Bibliographic record
Abstract
Chemotherapy-induced peripheral neuropathy (CIPN) results from damage to or dysfunction of the peripheral nerves. The development of CIPN is anticipated for the majority of breast cancer patients who receive neurotoxic chemotherapy, depending on the agent used, dose, and schedule. Sensory symptoms often predominate and include numbness, tingling, and distal extremity pain. Weakness, gait impairment, loss of functional abilities, and other deficits may develop with more severe CIPN. This article outlines a prospective surveillance model for physical rehabilitation of women with breast cancer who develop CIPN. Rehabilitative efforts for CIPN start at the time of breast cancer diagnosis and treatment planning. The prechemotherapy evaluation identifies patients with preexisting peripheral nervous system disorders that may place them at higher risk for the development of CIPN. This clinical evaluation should include a history focusing on symptoms and functional activities as well as a physical examination that objectively assesses the patient's strength, sensation, reflexes, and gait. Ongoing surveillance following the initiation of a neurotoxic agent is important to monitor for the development and progression of symptoms associated with CIPN, and to ensure its resolution over the long term. CIPN is managed best by a multidisciplinary team approach. Early identification of symptoms will ensure appropriate referral and timely symptom management. The prospective surveillance model promotes a patient-centered approach to care, from pretreatment through survivorship and palliative care. In this way, the model offers promise in addressing and minimizing both the acute and long-term morbidity associated with CIPN.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".