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Record W1980377648 · doi:10.1002/jso.20632

Chemotherapy for colorectal cancer prior to liver resection for colorectal cancer hepatic metastases does not adversely affect peri‐operative outcomes

2006· article· en· W1980377648 on OpenAlexaff
Ajay Sahajpal, Charles M. Vollmer, Elijah Dixon, Elisa Chan, Alice C. Wei, Mark S. Cattral, Bryce Taylor, David Grant, Paul D. Greig, Steven Gallinger

Bibliographic record

VenueJournal of Surgical Oncology · 2006
Typearticle
Languageen
FieldMedicine
TopicHepatocellular Carcinoma Treatment and Prognosis
Canadian institutionsMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsMedicineColorectal cancerAffect (linguistics)ChemotherapyOncologyPeriInternal medicinePerioperativeHepatectomyResectionCancerGeneral surgerySurgery

Abstract

fetched live from OpenAlex

BACKGROUND: Systemic chemotherapy is being used increasingly in patients with colorectal cancer. The effects of prior systemic adjuvant or palliative chemotherapy on morbidity following hepatic resection for metastases are not well defined. OBJECTIVES: To assess the peri-operative impact of systemic chemotherapy on liver resection for colorectal cancer hepatic metastases. METHODS: Ninety-six resections for colorectal cancer hepatic metastases performed from July 2001 to July 2003 (93% > or =2 segments) were reviewed. Pre-operative demographics, peri-operative features, and post-operative outcomes were collected prospectively. Type of chemotherapy and the temporal relationship of chemotherapy to the liver resection were analyzed. RESULTS: Fifty-three of 96 patients (55%) received a mean of 5.7 cycles (6.1 months) of systemic chemotherapy prior to hepatic resection, with a median interval of 12 months from end of chemotherapy to liver resection (range 1-75 months). Thirty-five received 5-fluorouracil/leucovorin (5-FU/LV) alone, nine had irinotecan (CPT-11) in addition to 5-FU/LV, and nine were not specified. Pre-operative age, sex, co-morbidities, ASA score, biochemical and liver enzyme profiles, tumor number, and extent and technique of hepatic resection were the same in the chemotherapy and non-chemotherapy cohorts. Mean tumor size was smaller (4.5 cm vs. 5.8 cm) and synchronous metastases were half as common (25% vs. 49%) in the chemotherapy group. Liver resection operative time was equivalent (270 min) in the two groups. Higher estimated blood loss (EBL) (1,000 ml vs. 850 ml), but fewer transfusions (23% vs. 15%) were associated with the chemotherapy group. Although not statistically significant, post-operative liver enzyme peaks were higher in the chemotherapy group (AST = 402 U/L vs. 302 U/L, P = 0.09 and ALT = 433 U/L vs. 312 U/L, P = 0.1). Peak changes in INR and serum bilirubin did not differ. Complications and length of stay (LOS) did not differ between the groups. The only post-operative death was in the non-chemotherapy group. Interestingly, hepatic steatosis was present in 28% of the non-chemotherapy cases and 57% of the chemotherapy resection specimens (P = 0.005) and was marked (>30%) in 7% and 10%, respectively. Further analysis of the chemotherapy group based on the interval between completion of chemotherapy and the hepatic resection (<6 months, 7-12 months, 1-2 years, and >2 years) revealed a trend towards worse outcomes in most categories for those in the >2 years cohort. When comparing the 5-FU/LV alone, to the CPT-11 group there were no significant differences except higher intra-operative blood loss in the group receiving 5-FU/LV alone (1,295 ml vs. 756 ml, P = 0.01). CONCLUSION: Despite variations in biochemical function and hepatic steatosis, short-term clinical outcomes are not affected by the administration of chemotherapy prior to hepatic resection. Furthermore, there is no detrimental effect of close timing of chemotherapy prior to resection, and there are no appreciable differences between irinotecan containing regimes and more traditional 5-FU-only based therapies, although the subset sample sizes were small in this study.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.602
Threshold uncertainty score0.672

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.050
GPT teacher head0.353
Teacher spread0.304 · 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

Citations58
Published2006
Admission routes1
Has abstractyes

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