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Record W1996691546 · doi:10.1097/mpa.0b013e31821fd70b

Safety and Feasibility of Pancreaticoduodenectomy in the Elderly

2011· article· en· W1996691546 on OpenAlexaff
Valéria De Franco, Éric Frampas, Mark T. C. Wong, G. Meurette, Marion Charvin, J Leborgne, Nicolas Régenet

Bibliographic record

VenuePancreas · 2011
Typearticle
Languageen
FieldMedicine
TopicPancreatic and Hepatic Oncology Research
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsMedicinePancreaticoduodenectomyPerioperativeBody mass indexAmerican society of anesthesiologistsCohortSurgeryInternal medicineResection

Abstract

fetched live from OpenAlex

OBJECTIVES: To compare the clinical outcomes after pancreaticoduodenectomy (PD) in patients older than 70 years old against a matched cohort of patients younger than 70. METHODS: A search of the department database revealed that 285 consecutive patients underwent PD from 1996 to 2009. Forty-one patients (14%) were identified to be older than 70 years (group 1), and they were matched with 41 patients younger than 70 (group 2) according to sex, body mass index, American Society of Anesthesiologists score and tumor staging. Medical comorbidities, preoperative CA19-9 and hemoglobin levels, operative and histopathologic data, postoperative course, and survival outcomes were compared between the 2 groups of patients. RESULTS: Statistical analyses revealed no significant difference between the 2 groups, except for preoperative CA19-9 and hemoglobin levels, operating time, duration of hospitalization, and the number of lymph nodes removed. These parameters, however, did not have an impact on morbidity, mortality, and overall survival. CONCLUSIONS: Based on our study, perioperative morbidity, mortality, and overall survival are not poorer in patients older than 70. Thus, PD should not be contraindicated solely on the basis of chronological age. Moreover, PD can be rationally proposed to patients meeting the "fit elderly" definition.

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 score0.083
Threshold uncertainty score0.311

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.086
GPT teacher head0.354
Teacher spread0.268 · 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

Citations24
Published2011
Admission routes1
Has abstractyes

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