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Extensive lower limb injuries in a child complicated by complex pain management and profound anemia

2008· article· en· W2011898086 on OpenAlexaff
Giuliana Rizzo, Marinella Astuto, Davinia E. Withington

Bibliographic record

VenuePediatric Anesthesia · 2008
Typearticle
Languageen
FieldMedicine
TopicPediatric Pain Management Techniques
Canadian institutionsMcGill UniversityMcGill University Health CentreMontreal Children's Hospital
Fundersnot available
KeywordsMedicineAnemiaPhysical therapySurgeryInternal medicine

Abstract

fetched live from OpenAlex

We describe a case of severe pain associated with extensive lower limb injures in a 5-year old, complicated by profound anemia in a Jehovah's Witness family. The study was carried out in a pediatric intensive care unit of a tertiary level university hospital. The patient was 5-year-old girl, with multiple open fractures and extensive soft tissue loss on her left foot and ankle due to a lawnmower injury leading to severe pain and profound anemia with management of the latter complicated by family beliefs. The interventions given were multi-modal pain management and treatment of severe anemia with avoidance of transfusion. A drop in hemoglobin from 11.6 g.dl(-1) at admission to a nadir of 4.3 g.dl(-1) on day 7 was observed. Effective pain control was achieved with nurse- and then patient-controlled analgesia plus adjuncts. Effective pain management and control of anxiety can be achieved by a multi-modal approach in young children. Profound anemia was treated without transfusion and without compromise of tissue healing.

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)0.002
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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.246
Teacher spread0.231 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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