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Record W827940946

Laparoscopy-Assisted Distal Gastrectomy for the Eldest Elderly Patients with Gastric Cancer.

2014· article· en· W827940946 on OpenAlexaff
Yoshihiro Miyasaka, Toshinaga Nabae, Chikashige Yanagi, Takaharu Yasui, Masahiko Kawamoto, Mikimasa Ishikawa, Akihiko Uchiyama

Bibliographic record

VenuePubMed · 2014
Typearticle
Languageen
FieldMedicine
TopicGastric Cancer Management and Outcomes
Canadian institutionsPancreas Centre (Canada)
Fundersnot available
KeywordsMedicineGastrectomyLaparoscopyCancerSurgeryBlood lossBody mass indexSignificant differenceGeneral surgeryInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND/AIMS: The number of the eldest elderly (aged 85 years and older) patients with gastric cancer has been rising in Japan. Laparoscopy-assisted distal gastrectomy (LADG) has been accepted as a less invasive treatment for gastric cancer. The purpose of this study is to evaluate the efficacy and safety of LADG for eldest elderly patents. METHODOLOGY: From January 2006 to July 2010, 262 patients underwent LADG for gastric cancer. Of these, 9 patients were 85 years old and over (eldest elderly group) and the remaining 253 patients were younger than 85 years (control group). Clinicopathological characteristics and operative outcomes were analyzed. RESULTS: Among clinicopathological characteristics analyzed in this study (gender, body mass index, co-morbidity, American Society of Anesthesiologists physical status and tumor status), only gender showed a significant difference between the eldest elderly and the control groups. There were no significant differences in operation time, blood loss, postoperative complication and postoperative hospital stay between the 2 groups. No serious complications or mortality were found in the eldest elderly group. CONCLUSIONS: It is suggested that LADG is a safe and efficient procedure for the treatment of gastric cancer, even in eldest elderly patients.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.389
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.016
GPT teacher head0.234
Teacher spread0.219 · 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
Published2014
Admission routes1
Has abstractyes

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