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Record W3163846989 · doi:10.1002/adtp.202000267

The Effects of Localized Heat on the Hallmarks of Cancer

2021· article· en· W3163846989 on OpenAlex

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

VenueAdvanced Therapeutics · 2021
Typearticle
Languageen
FieldMedicine
TopicInfrared Thermography in Medicine
Canadian institutionsTrinity College
FundersHorizon 2020 Framework ProgrammeIrish Research Council
KeywordsMetastasisCancerMedicineCancer researchCancer treatmentImmune systemTumor cellsImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Heat has been used to treat tumors for thousands of years. There are reports of the Egyptians and Greek philosophers using such treatments as far back as 3000 BC and 500 BC respectively for various solid tumors. Albeit, in these cases, the treatment was not very controlled and consisted of hot sticks or blades placed against tissue in order to thermally ablate the tumor. It was not until recent times that the application of heat through various mediums enabled a more controlled, localized, and consistent method of treating tumors. While the therapeutic potential of this treatment has become more apparent, the mechanisms related to its efficacy are only recently beginning to surface. This review discusses the evidence associated with the effects of localized heat on the hallmarks of cancer. Key literature describing modulations to vasculature, cell viability, DNA damage and repair, metabolism, immune system, and tumor metastasis in response to heat will be reviewed along with considerations for its optimal implementation in the clinic to enhance the efficacy of conventional treatments.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.213
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.304
Teacher spread0.293 · 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