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Record W4293778660 · doi:10.3892/ol.2022.13454

Long‑term survival in a patient with extensive‑stage small cell lung cancer treated with multiple courses of salvage stereotactic radiation after whole brain radiotherapy: A case report

2022· article· en· W4293778660 on OpenAlexafffund
Andrea Vucetic, Belal Ahmad, Terence Tang

Bibliographic record

VenueOncology Letters · 2022
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Research Studies
Canadian institutionsLondon Health Sciences CentreWestern University
FundersLondon Health Sciences Centre
KeywordsMedicineRadiation therapyRadiosurgeryLung cancerProphylactic cranial irradiationStereotactic radiation therapyStage (stratigraphy)Salvage therapyCancerRadiologySurgeryOncologyChemotherapyInternal medicine

Abstract

fetched live from OpenAlex

Intracranial recurrence following initial cranial irradiation for extensive-stage small cell lung cancer (ES-SCLC) can often be a treatment dilemma given the aggressive nature of the disease, the overall poor prognosis and concerns regarding re-treatment toxicity. The present report describes the case of a 62-year-old man diagnosed with ES-SCLC and synchronous brain metastases who initially underwent whole brain radiotherapy, chemotherapy and consolidative thoracic radiotherapy. The patient was found to have a solitary intracranial recurrence at both 3.5 and 6 years after his diagnosis. On both occasions, the patient received salvage stereotactic radiation, 30 Gy in 5 fractions, and continues to remain functionally independent. Overall, the present case demonstrates that with the appropriate patient selection, aggressive local salvage of recurrent intracranial ES-SCLC with stereotactic radiation can yield excellent and durable clinical outcomes.

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)0.000
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: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0020.001
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 head0.014
GPT teacher head0.315
Teacher spread0.301 · 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 designCase report
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

Citations4
Published2022
Admission routes2
Has abstractyes

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