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Record W3034274609

Medication management and pharmacokinetic changes after bariatric surgery.

2020· review· en· W3034274609 on OpenAlexaffabout
Sabrina Lorico, Blaine Colton

Bibliographic record

VenuePubMed · 2020
Typereview
Languageen
FieldMedicine
TopicBariatric Surgery and Outcomes
Canadian institutionsAlberta Health ServicesRed Deer Regional Hospital
Fundersnot available
KeywordsPharmacokineticsMedicineComputer scienceObesity SurgeryBioinformaticsData scienceObesityGastric bypassPharmacologyInternal medicineWeight lossBiology
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVE: To identify expected pharmacokinetic changes and provide practical recommendations for the medication management of chronic disease states after bariatric surgery. SOURCES OF INFORMATION: . Reference lists of original studies and reviews were also hand searched. Included studies were entered into PubMed and articles under the "Similar articles" heading were also reviewed. Only studies relevant to bariatric surgery types currently available in Canada (ie, Roux-en-Y gastric bypass, sleeve gastrectomy, or gastric banding) were included. MAIN MESSAGE: Pharmacokinetic changes anticipated after bariatric surgery vary by surgery type. There are several guiding principles that might be applied to medication management regimens after bariatric surgery. Practice tips are also presented for medication management of specific chronic disease states. CONCLUSION: Changes to long-term medication regimens after bariatric surgery should be anticipated and managed in an appropriate and timely manner. The provided clinical practice recommendations might be used in conjunction with a patient's clinical picture to adjust chronic medication regimens in an appropriate and evidence-based manner after bariatric surgery.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
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 head0.054
GPT teacher head0.287
Teacher spread0.232 · 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 designNot applicable
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

Citations37
Published2020
Admission routes2
Has abstractyes

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