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The Evolution of Medical Technology

2001· article· en· W2089307895 on OpenAlexaboutno aff
Stuart A. Green

Bibliographic record

VenueClinical Orthopaedics and Related Research · 2001
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineClearanceFood and drug administrationDiversification (marketing strategy)Risk analysis (engineering)Product (mathematics)New product developmentProcess (computing)Operations managementIntensive care medicineSurgeryMarketingComputer scienceBusinessEngineering

Abstract

fetched live from OpenAlex

Many forthcoming medical advances-growth factors, tissue engineering, gene therapy, attachable prosthetic limbs, and implantable computers--are so new that as yet there is no clinical experience with them. Each therapeutic technique will evolve in an environment containing few guideposts to help judge its efficacy and safety. Recent developments in evolution theory (based on an analysis of Cambrian fossils in Canada's Burgess Shale quarry) suggest that evolution passes, at times, through innovative cycles of progress--when diversification of design leads to perfection of form--with the concomitant production of many unsuccessful models. The evolution of the total knee replacement is a perfect example of the process, because many of the early devices have proven to be dismal failures. However, modern knee replacements would not have been developed without them. Because the risk of unforeseen complications associated with new medical products cannot be discerned in advance, each patient-consumer should have the opportunity to intelligently weigh an innovative product's risk potential against its possible benefit. The proposal made here, for a temporary New Product status for new drugs and devices after a product is cleared by the Food and Drug Administration for general marketing, provides a mechanism for making such decisions.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.868
Threshold uncertainty score0.562

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.038
GPT teacher head0.386
Teacher spread0.348 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
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
Published2001
Admission routes1
Has abstractyes

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