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Record W172659617

Central laboratory versus point of care testing in intraoperative monitoring of parathyroid hormone levels: cost comparison.

2008· article· en· W172659617 on OpenAlexaffabout
Daniel A. O’Connell, Hadi Seikaly, Jeffrey Harris

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsParathyroid hormoneMedicinePoint of carePoint-of-care testingTechnicianParathyroidectomyPatient careInternal medicinePathologyEngineeringNursing
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: To examine the cost of central laboratory-based intraoperative parathyroid hormone (PTH) during parathyroid surgery compared with a point of care-based PTH testing system. METHODS: Based on a retrospective analysis of intraoperative PTH testing in 50 parathyroid surgeries, a cost comparison between the current testing system at a university-affiliated tertiary care facility (Elecsys 1010, Roche Diagnostics, Basel, Switzerland) and a theoretical model using the QuiCK-intraoperative intact PTH system (Nichols Institute Diagnostics, San Juan Capistrano, CA) was generated. RESULTS: The cost per surgery of central laboratory-based PTH testing was $129.15 compared with $550.98 for the point of care-based system. Costs were calculated accounting for the purchase price of equipment, cost of reagents and processing, and laboratory technician time. CONCLUSIONS: This is the first cost comparison study using a Canadian-based health care model for point of care versus central laboratory PTH testing and adds to a very limited number of cost comparison studies on this topic. This study provides evidence that in the setting of a tertiary care facility that has on-site laboratory facilities with dedicated staff, central laboratory-based PTH assays provide an efficient and cost-effective way of monitoring PTH levels during parathyroidectomy 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 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)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.046
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.019
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.096
GPT teacher head0.303
Teacher spread0.207 · 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 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

Citations13
Published2008
Admission routes2
Has abstractyes

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