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Record W3013157909 · doi:10.14740/jmc3447

A Challenging Cesarean Delivery and Perioperative Course in a Former Polysubstance Abuser Without the Use of Traditional Opioids

2020· article· en· W3013157909 on OpenAlexvenueno aff
D. Gutman, Jennifer R. Matos, Christopher A. Skorke

Bibliographic record

VenueJournal of Medical Cases · 2020
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePolysubstance dependencePerioperativeOpioidNarcoticPopulationAnesthesiaIntensive care medicineSubstance abusePsychiatryInternal medicine

Abstract

fetched live from OpenAlex

Substance abuse is a major challenge in the United States. According to the Human Resources and Services Administration, we are in an opioid crisis with over 130 people a day dying from opioid-related drug overdoses. As awareness of this epidemic has grown, there has been an increase in patients coming in for surgery requesting a narcotic-free anesthetic. This presents both a challenge and an opportunity for the anesthesiologist who has a duty to respect the patient's autonomy while simultaneously achieving the appropriate perioperative outcome. The considerations are especially important in the vulnerable population of pregnant women.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0030.001

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 head0.090
GPT teacher head0.307
Teacher spread0.217 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations0
Published2020
Admission routes1
Has abstractyes

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