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Record W2005975697 · doi:10.14740/jocmr2086w

Comparative Changes in Tissue Oxygenation Between Laparoscopic and Open Cholecystectomy

2015· article· en· W2005975697 on OpenAlexvenueno aff
George Bablekos, Stylianos Michaelides, Antonis Analitis, Maria Lymperi

Bibliographic record

VenueJournal of Clinical Medicine Research · 2015
Typearticle
Languageen
FieldMedicine
TopicAbdominal Surgery and Complications
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineLaparoscopic cholecystectomyCholecystectomyOxygenationMorningOpen cholecystectomyAnestheticAnesthesiaSurgeryGastroenterologyInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Previous studies examined the effect of laparoscopic cholecystectomy (LC) versus open cholecystectomy (OC) on physiological variables of the respiratory system. In this study we compared changes in arterial blood gases-related parameters between LC and OC to assess their comparative influence on gas exchange. METHODS: We studied 28 patients, operated under identical anesthetic protocol (LC: 18 patients, OC: 10 patients). Measurements were made on the morning before surgery (BS), the second (AS2) and the eighth (AS8) postoperative day. Studied parameters, including alveolar-arterial difference in PO2 ((A-a)DO2) and oxygen content (Oct in vol%), were statistically compared. RESULTS: On AS2 a greater increase was found in ((A-a)DO2) for the OC compared to LC (4.673 ± 0.966 kPa versus 3.773 ± 1.357 kPa, respectively). Between BS and AS2, Oct in vol% decreased from 17.55 ± 1.90 to 15.69 ± 1.88 in the LC and from 16.99 ± 2.37 to 14.62 ± 2.23 in the OC, whilst a reduction (P = 0.093) between AS2 and AS8 was also found for the open method. Besides, on AS2, SaO2% decrease was greater in OC compared to LC (P = 0.096). CONCLUSIONS: On AS2, the greater increase in OC-((A-a)DO2) associated with Oct in vol% and SaO2% findings also in OC group suggest that LC might be associated with lower risk for impaired tissue oxygenation.

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)0.013
metaresearch head score (Gemma)0.005
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 score0.118
Threshold uncertainty score0.629

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.713
GPT teacher head0.660
Teacher spread0.053 · 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
Published2015
Admission routes1
Has abstractyes

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