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Record W2409430773 · doi:10.1055/s-2004-824887

Should the Donor Radius be Plated Prophylactically after Harvest of a Radial Osteocutaneous Flap? A Cost-Effectiveness Analysis

2004· article· en· W2409430773 on OpenAlexaff
Gloria Rockwell, Achilleas Thoma

Bibliographic record

VenueJournal of Reconstructive Microsurgery · 2004
Typearticle
Languageen
FieldMedicine
TopicReconstructive Surgery and Microvascular Techniques
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsMedicineSurgeryPlating (geology)RADIUSChristian ministryCost analysisQuality-adjusted life yearComplicationCost effectivenessRisk analysis (engineering)Operations research

Abstract

fetched live from OpenAlex

The objective of this study was to assess the cost-effectiveness of prophylactic plating of the donor radius after harvest of a radial osteocutaneous flap. Costs were measured from a Ministry of Health perspective, and effectiveness in terms of quality adjusted life years (QALYs.) A literature search identified 22 studies reporting complications of radial osteocutaneous free flaps, and nine studies reporting complications of radius plating. The rates of the various complications were pooled to provide the probability of each clinically important complication. A decision analytic model was used to determine the total costs, and QALYs for each of the treatment options. The expected direct costs for prophylactic plating and treatment after fracture were $2071 and $140, respectively. The expected QALYs for prophylactic plating and treatment after fracture were 8.55 and 9.92, respectively. It was concluded that prophylactic plating of the donor radius is not a cost-effective approach when compared to the treatment of donor radius fractures after they occur.

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)0.007
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.004
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.284
Teacher spread0.260 · 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 designSimulation or modeling
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

Citations19
Published2004
Admission routes1
Has abstractyes

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