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Record W939395412 · doi:10.1503/cjs.006413

Prognostic factors for morbidity and mortality in elderly patients undergoing acute gastrointestinal surgery: a systematic review

2014· review· en· W939395412 on OpenAlexaffvenue
Philip J. Davis, Jill A. Hayden, Jeremy E. Springer, Jonathan G. Bailey, Michele Molinari, Paul M. Johnson

Bibliographic record

VenueCanadian Journal of Surgery · 2014
Typereview
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMedicineOdds ratioConfidence intervalMEDLINEInternal medicineMeta-analysisCohort studyIntensive care medicineEmergency medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Elderly patients undergoing acute gastrointestinal (GI) surgery experience increased morbidity and mortality compared with younger and elective patients. Prognostic factors can be used to counsel patients of these risks and, if modifiable, to minimize them. We reviewed the literature on prognostic factors for adverse outcomes in elderly patients undergoing acute GI surgery. METHODS: We searched PubMed and Embase using a strategy developed in collaboration with an expert librarian. Studies examining independent associations between prognostic factors and morbidity or mortality in patients aged 65 and older undergoing acute GI surgery were selected. We extracted data using a standardized form and assessed study quality using the QUIPS tool. RESULTS: Nine cohort studies representing 2958 patients satisfied our selection criteria. All studies focused on postoperative mortality. Thirty-four prognostic factors were examined, with significant variability across studies. There was limited or conflicting evidence for most prognostic factors. Meta-analysis was only possible for the American Society of Anesthesiologists (ASA) score, which was found to be associated with mortality in 4 studies (pooled odds ratio 2.77, 95% confidence interval 0.92-8.41). CONCLUSION: While acute GI surgery in elderly patients is becoming increasingly common, the literature on prognostic factors for morbidity and mortality in this patient population lags behind. Further research is needed to help guide patient care and potentially improve outcomes.

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)0.003
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.357
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0100.003
Bibliometrics0.0010.000
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 head0.081
GPT teacher head0.319
Teacher spread0.238 · 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 designSystematic review
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

Citations42
Published2014
Admission routes2
Has abstractyes

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