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Record W2778029131 · doi:10.1093/ajcp/aqx088

Cost Analysis of Operating an Anatomic Pathology Laboratory in a Middle-Income Country

2017· article· en· W2778029131 on OpenAlexaff
Phaik‐Leng Cheah, Lai‐Meng Looi, Susan Horton

Bibliographic record

VenueAmerican Journal of Clinical Pathology · 2017
Typearticle
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWorkloadMedicineClinical pathologySurgical pathologyMedical laboratoryMedical diagnosisCost analysisAnatomical pathologyCellular pathologyPathologyEmergency medicineOperations researchComputer science

Abstract

fetched live from OpenAlex

Objective: To examine the cost of operating an anatomic pathology laboratory in a teaching hospital in Malaysia. Once the cost is determined, compare it with the costs of operating other laboratories in the same hospital, and operating anatomic pathology laboratories in other countries. Methods: Cost and workload data were obtained from hospital records for 2015. Time allocation of staff between laboratory testing and other activities was determined using assumptions from published workload studies. Results: The laboratory received 20,093 cases for testing in 2015, and total expenditures were US $1.20 million, ie, $61.97 per case. The anatomic pathology laboratory accounted for 5.2% of the laboratory budget at the hospital, compared to 64.3% for the clinical laboratory and 30.5% for the microbiology laboratory. We provide comparisons to a similar laboratory in the United States. Conclusions: Anatomic pathology is more costly than other hospital laboratories due to the labor-intensive work, but is essential, particularly for cancer diagnoses and treatment.

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)0.011
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.128
GPT teacher head0.497
Teacher spread0.369 · 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 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

Citations8
Published2017
Admission routes1
Has abstractyes

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