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Isolated limb infusion: efficacy, toxicity and an evolution in the management of in-transit melanoma

2015· article· en· W132476954 on OpenAlexaffabout
Laura Chin‐Lenn, Claire Temple‐Oberle, J. Gregory McKinnon

Bibliographic record

VenuePlastic Surgery · 2015
Typearticle
Languageen
FieldMedicine
TopicCutaneous Melanoma Detection and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineSurgeryMelanomaToxicityQuality of life (healthcare)Progressive diseaseRetrospective cohort studyPercutaneousChemotherapyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Isolated limb infusion (ILI) delivers low-flow chemotherapy via percutaneous catheters to treat melanoma in-transit metastases. OBJECTIVE: To describe the experience of two regional referral centres with ILI. METHODS: A retrospective review of patients who underwent ILI between 2002 and 2012 was performed. Outcomes were measured using the WHO criteria for response, the Wieberdink toxicity score and long-term limb function using the Toronto Extremity Salvage Score (TESS). RESULTS: Fifty-two patients (mean age 66 years [range 27 to 90 years], female sex 65%, and lower [treated] limb in 86%) with 54 ILIs were reviewed. Wieberdink toxicity score was ≥3 in 21 (39%) procedures. Median follow-up was 18 months (range one to 117 months). Initial complete response (CR) was 29%, partial response 27%, stable disease 18% and progressive disease 27%. Predictors of better initial response were low disease burden and previous treatment. One or more treatments after ILI were common (65%). At 12 months, 19% of ILI patients had died from melanoma but 44% of surviving patients experienced limb CR. At 24 months, 57% of surviving patients experienced limb CR. The quality of life in the surviving, contactable patients according to the Toronto Extremity Salvage Score was 89%. CONCLUSION: Even if ILI does not result in CR for melanoma intransit metastases. it may slow disease progression as a single therapy, but more frequently in combination with other modalities.

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 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.162
Threshold uncertainty score0.291

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.026
GPT teacher head0.253
Teacher spread0.227 · 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 teacher head, 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

Citations5
Published2015
Admission routes2
Has abstractyes

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