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Undesirable Outcomes of Starvation Therapy of Cancer Require Special Attention

2020· article· en· W3003441679 on OpenAlexaff
Fawwaz Shakir Al Joudi

Bibliographic record

VenueJournal of Advances in Medicine and Medical Research · 2020
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced organ toxicity mitigation
Canadian institutionsMcMaster University
Fundersnot available
KeywordsStarvationCancerClinical trialCancer therapyMedicineIntensive care medicinePsychologyPsychotherapistPathologyInternal medicine

Abstract

fetched live from OpenAlex

Nutritional starvation is a growing area of research into development of cancer therapy. Within the vast amount of positive research findings in starvation trials, there have been weaknesses in some of the systems utilized. Because such weaknesses are taken as adverse points that must be well-thought-out and avoided, such negative effects have been sought from the literature and presented in this work. This mini-review can then be a suitable guide for researchers and clinicians to either avoid situations where the growth of certain cancer cells is enhanced by certain forms or modes of starvation, or their metastatic abilities are boosted. The intra- and extra-cellular mechanisms associated with these cellular enhancements have been demonstrated. Some negative interactions of starvation with chemotherapy have also been included. The understanding of these mechanisms can help avoid them for better future experimental and clinical results and may, at the same time, open new avenues for research workers to find ways of dismantling them.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.161
GPT teacher head0.498
Teacher spread0.337 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2020
Admission routes1
Has abstractyes

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Same venueJournal of Advances in Medicine and Medical ResearchSame topicChemotherapy-induced organ toxicity mitigationFrench-language works237,207