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Record W4406311695 · doi:10.1007/978-1-59259-659-1_4

Quality Control

2004· book-chapter· en· W4406311695 on OpenAlexaboutno aff
Sydney Lou Bonnick

Bibliographic record

VenueHumana Press eBooks · 2004
Typebook-chapter
Languageen
FieldMedicine
TopicClinical Laboratory Practices and Quality Control
Canadian institutionsnot available
Fundersnot available
KeywordsQuality (philosophy)Computer sciencePhilosophyEpistemology

Abstract

fetched live from OpenAlex

Although much has been written about quality control procedures in densitometry, much of this literature has been concerned with data collection in clinical research rather than patient data collected as part of medical care. Quality control, although absolutely necessary in clinical research, is no less necessary in clinical practice. The original indications for bone mass measurements from the National Osteoporosis Foundation published in 1988 and the guidelines for the clinical applications of bone densitometry from the International Society for Clinical Densitometry published in 1996 called for strict quality control procedures at clinical sites performing densitometry (1,2). The Canadian Panel 1 of the International Society for Clinical Densitometry published specific guidelines for quality control procedures in 2002 (3). Such procedures are crucial to the generation of accurate and precise bone density data. When quality control is poor or absent, the bone density data may be incorrect. The interpretation made by the physician based on that incorrect information would be in error. The medical management of the patient may be adversely affected. The patient will also have been exposed to a small amount of radiation inappropriately and wasted time and money. In clinical trials, the results from hundreds or thousands of individuals are usually averaged and conclusions based on the average values. Small errors in machine performance are made insignificant by the averaging of so many results. In clinical practice, this luxury does not exist. Decisions are made based on one measurement from one patient, which means that strict quality control in clinical practice is even more important than in clinical trials. Despite inherently superb accuracy and precision in today’s densitometers, alterations in the functioning of the machines can and will occur. Quality control procedures to detect these alterations in machine function should be utilized by every clinical site performing densitometry regardless of the frequency with which measurements are performed.

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)0.228
metaresearch head score (Gemma)0.462
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.228
Threshold uncertainty score0.952

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2280.462
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0050.004
Bibliometrics0.0120.013
Science and technology studies0.0050.005
Scholarly communication0.0170.007
Open science0.0080.009
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0820.032

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 head0.204
GPT teacher head0.405
Teacher spread0.200 · 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.

Study designNot applicable
Domainnot available
GenreReview

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
Published2004
Admission routes1
Has abstractyes

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