Diagnostic tools should be used for the diagnosis of chemotherapy induced peripheral neuropathy in breast cancer patients receiving taxanes
Bibliographic record
Abstract
BACKGROUND: Though the incidence, characteristics, and pathogenesis of chemotherapy induced peripheral neuropathy (CIPN) by taxane based chemotherapy were extensively studied, diagnostic guidelines extent only recently. AIM: To observationally investigate whether specific tests can be used to predict and monitor CIPN severity. METHODS: Fourteen female breast cancer patients receiving paclitaxel or docetaxel were evaluated using the McGill Pain Questionnaire (MPQ), National Cancer Institute Common Toxicity Criteria (NCI-CTC) grading, clinical total neuropathy score (TNSc), quantitative sensory testing (QST) of pressure pain threshold (PPT), and numeric rating scale (NRS) scores and stocking and glove distribution testing (SGDT), at the start (T0), midst (T1), and end (T2) of their treatment and after 3 months (T3). RESULTS: At T3, patients scored NCI-CTC neuropathy grade 1 (14.3%), 2 (64.3%), and 3 (14.3%) respectively. Fifty percentage scored at least grade 1 at T0, with complaints not caused by CIPN. Pain, if present, was denominated "tingling" and "cold" in the MPQ. Median TNSc score increased from T0 (2.43) to T1 (4.71) to T2 (5.50) to T3 (5.57), as did pinprick and cold sensation disturbances in SGDT. PPT and associated NRS remained unchanged. TNSc and SGDT at T1 could not predict the NCI-CTC grade at T3. CONCLUSION: NCI-CTC, TNSc, and stocking and glove distribution testing can be used in the early diagnosis and monitoring of CIPN, with false-positive findings at baseline. Final NCI-CTC grades could not be predicted.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".