Stratégie de recherche pour : Anti-Cancer Drug Induced Peripheral Neuropathy (ACDIPN) in Cancer Patients Using Genomic Sequencing Technologies: A Scoping Review
Bibliographic record
Abstract
English : The Notes field includes the complete search strategy executed in Embase (via Embase.com), focusing on genomic sequencing technologies and anti-cancer drug-induced peripheral neuropathy (ACDIPN). The files in this dataset include the complete search strategies for Embase (via Embase.com), MEDLINE (via Ovid), CINAHL (via EBSCO), the Cochrane Library (via cochranelibrary.com), and the Web of Science Core Collection as provided by Université Laval, as well as the complementary searches conducted in Google Scholar and Semantic Scholar using Elicit and Consensus. Initial Search date : 2025-02-20. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - Français : Le champ Remarques contient la stratégie de recherche complète effectuée dans Embase (via Embase.com), portant sur les technologies de séquençage génomique et la neuropathie périphérique induite par les médicaments anticancéreux (ACDIPN). Les fichiers de ce jeu de données incluent les stratégies de recherche complètes pour Embase (via Embase.com), MEDLINE (via Ovid), CINAHL (via EBSCO), la Cochrane Library (via cochranelibrary.com) et la Web of Science Core Collection, telles que fournies par l’Université Laval, ainsi que les recherches complémentaires effectuées dans Google Scholar et Semantic Scholar à l’aide des outils Elicit et Consensus. Date de la recherche initiale : 2025-02-20.
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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 | 0.035 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".