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Record W2067881562 · doi:10.1586/14737140.1.4.585

Molecular and pharmacological strategies to overcome multidrug resistance

2001· review· en· W2067881562 on OpenAlex
Jennifer A. Shabbits, Rajesh Krishna, Lawrence D. Mayer

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueExpert Review of Anticancer Therapy · 2001
Typereview
Languageen
FieldMedicine
TopicDrug Transport and Resistance Mechanisms
Canadian institutionsBC Cancer Agency
Fundersnot available
KeywordsMultiple drug resistanceMedicineDrug resistanceIntensive care medicineRisk analysis (engineering)Biology

Abstract

fetched live from OpenAlex

Multidrug resistance is a major obstacle to the effective treatment of cancer. Despite vast improvements in our understanding of the mechanisms of drug resistance, relatively few significant advances have been made towards effectively circumventing it in a clinical setting. The ability to modulate multidrug resistance has been complicated by the fact that many human tumors simultaneously exhibit multiple resistance mechanisms. In order to effectively overcome multidrug resistance it will be necessary to design new strategies that combine multiple modulating agents and approaches. This review provides an overview of the major causes of multidrug resistance and summarizes many of the current approaches being taken to overcome it. We also describe how liposomal drug delivery systems can be utilized to aid in achieving these goals.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.894
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.045
GPT teacher head0.416
Teacher spread0.372 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it