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Record W2395335952

On the Net : A cornucopia of medical images on the Net

2001· article· en· W2395335952 on OpenAlexvenueno aff
Michael O’Reilly

Bibliographic record

VenueCanadian Medical Association Journal · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsnot available
Fundersnot available
KeywordsBookmarkingPresentation (obstetrics)UploadThe InternetComputer scienceMultimediaWorld Wide WebInternet privacyMedicineSurgery
DOInot available

Abstract

fetched live from OpenAlex

Two months ago you were asked to give a lecture on the immune system. No problem, you say. There's lots of time to find some snappy slides for your presentation. Two months pass, it's the morning of event and now you've got 2 hours to pull together some imagery that will keep that first-year medical class interested and informed. Naturally, you turn to the Net for help. And fortunately, lots of online repositories of medical imagery and media are freely available. In many the quality of the images is poor, but a few sites are worth bookmarking. The best is probably the Health on the Net (HON) Foundation's Medical Media Library (www.hon.ch/HONmedia/). HON, based in Geneva, grew out of a 1995 conference on telemedicine. It has since developed a number of Internet initiatives involving health care, including this impressive online media database. HONmedia contains more than 1950 medical images and videos covering 1200 themes and topics. Subjects range from anatomy to psychiatry, and this site isn't above injecting a bit of humour into its imagery, as shown by the short video Human macrophage ingesting candida albicans. The sound effects alone are worth the downloading effort. Another place to turn is the University of California's Medical Imaging Library (www-sci.lib.uci.edu/HSG /MedicalImage.html). This is a large database of teaching materials. It is not well organized but it does contain thousands of multimedia images and more than 700 health-science movies. If you want to spend a few bucks, several professional graphics houses sell quality images on the Net; Blausen Medical Communications (www.imagesofhealth.com) and the Medical Illustration Group (www.migroup.co.uk/m.i.g) are just 2 examples. A search for “medical images” in any search engine will turn up hundreds of results. You can also visit Yahoo's medical imagery directory (dir.yahoo.com /Health/Medicine/Medical_Imaging/). Finally, if you are just looking for some generic medical graphics to spruce things up, try the collection of sites at Web Clip Art (webclipart .about.com/internet/webclipart/msub35aa.htm). Most of these items are freely available, although some come with restrictions. As always, read the copyright information carefully. —

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.553
Threshold uncertainty score0.638

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0080.009
Open science0.0020.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.5530.399

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 head0.044
GPT teacher head0.351
Teacher spread0.307 · 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
GenreCommentary

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
Published2001
Admission routes1
Has abstractyes

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