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Record W2126286484 · doi:10.1097/cco.0b013e328011bec3

Small cell lung cancer and targeted therapies

2007· review· en· W2126286484 on OpenAlexaff
Fiona Blackhall, Frances A. Shepherd

Bibliographic record

VenueCurrent Opinion in Oncology · 2007
Typereview
Languageen
FieldMedicine
TopicLung Cancer Research Studies
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineLung cancerOncologyCancer research

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Small cell lung cancer is a chemosensitive malignancy, yet long-term survival remains elusive for the majority of patients. Here, we report on progress in evaluating novel targeted therapies for the treatment of this disease. RECENT FINDINGS: Interferons, matrix metalloproteinase inhibitors, thalidomide, bevacizumab, ZD6474, imatinib, gefitinib, oblimersen and aplidine have all entered clinical trial in patients with small cell lung cancer. Immunotherapy approaches targeting cell surface antigens such as CD-56 (BB10901) and GD3 ganglioside are also being evaluated. Interferons, matrix metalloproteinase inhibitors, imatinib and gefitinib have failed to demonstrate efficacy for this disease. Preliminary data for thalidomide are promising and so results from trials of other antiangiogenics such as bevacizumab and ZD6474 are awaited with interest. SUMMARY: Although the promise of targeted therapy has yet to be realized in patients with small cell lung cancer, the number of agents available for evaluation provides new optimism that progress will be made over the next decades.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.004

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 head0.240
GPT teacher head0.536
Teacher spread0.296 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations31
Published2007
Admission routes1
Has abstractyes

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