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Record W4400696964 · doi:10.37766/inplasy2024.7.0065

Effect of hyperthermic intraperitoneal chemotherapy on patients with advanced colorectal cancer: a systematic review and meta-analysis

2024· review· en· W4400696964 on OpenAlexaboutno aff
Ziying Su, Xiao Wang, Xiaosong Ru, Qiaoran Mao, F. Shi, Nuo Zhou, Luyao Xu

Bibliographic record

Venuenot available
Typereview
Languageen
FieldMedicine
TopicIntraperitoneal and Appendiceal Malignancies
Canadian institutionsnot available
Fundersnot available
KeywordsColorectal cancerMeta-analysisHyperthermic intraperitoneal chemotherapyMedicineOncologyChemotherapyIntraperitoneal chemotherapyInternal medicineCancerCytoreductive surgeryOvarian cancer

Abstract

fetched live from OpenAlex

Main outcome(s)The primary outcomes included o v e r a l l s u r v i v a l , r e c u r r e n c e r a t e , a n d complications.Quality assessment / Risk of bias analysis For randomised controlled trials, we used the Cochrane Risk of Bias Assessment Tool to assess the risk of bias, and for high-quality cohort studies, we used the Newcastle-Ottawa Scale (NOS).Strategy of data synthesis RevMan 5.3 was used to perform statistical analysis.The odds ratio (OR) was calculated for dichotomous data.We will use a random effects model, because differences in duration, frequency, and dose of HIPEC measures are unavoidable.Heterogeneity between studies will be examined using the Cochran Q statistic and the I measure.Results are presented in a forest plot. Subgroup analysisWe will perform subgroup analyses of HIPEC for the treatment of peritoneal metastases or the prevention of peritoneal metastases, randomised controlled trials or nonrandomised controlled trials, and the location of the colorectal cancer primary. Sensitivity analysisIn case of heterogeneity greater than 80%, we performed a sensitivity analysis and excluded studies with significant heterogeneity from the analysis. Country(ies) involvedChina.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Meta-epidemiology (broad)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.680
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0150.002
Bibliometrics0.0000.001
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 head0.016
GPT teacher head0.329
Teacher spread0.313 · 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.

Study designMeta-analysis
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

Citations0
Published2024
Admission routes1
Has abstractyes

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