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Record W374600142

Keeping Up-To-Date: The San Antonio CATs Initiative

2010· article· en· W374600142 on OpenAlexaboutno aff
John D. Rugh, William D. Hendricson, John P. Hatch, Birgit Junfin Glass

Bibliographic record

Venue˜The œJournal of the American College of Dentists · 2010
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare cost, quality, practices
Canadian institutionsnot available
Fundersnot available
KeywordsOral healthHealth careFamily medicineQuality (philosophy)Clinical PracticeMedicinePsychologyMedical educationPolitical scienceLaw
DOInot available

Abstract

fetched live from OpenAlex

One of the most significant challenges facing healthcare providers today is the problem of keeping up-to-date. Oral health knowledge is increasing exponentially (Figure 1), and although there is increased specialization, it is more and more difficult to keep up-to-date in any field. In addition, the useful half-life of biomedical knowledge, the time until half the knowledge in a field becomes obsolete or disproven, is only a few years. The result is that a great percentage of the knowledge gained in dental school is out-dated after only a few years in practice. Figure 1 Increase in oral health knowledge is changing our ability to keep up-to-date The problem of keeping up-to-date has been studied extensively in medicine. The results raise serious concerns. A systematic review of research dealing with time in practice and quality of care (Choudhry et al, 2005) concluded, “Physicians who have been in practice for more years and older physicians possess less factual knowledge, are less likely to adhere to appropriate standards of care, and may also have poorer patient outcomes.” Over one-half (52%) of the 62 studies reviewed found decreasing performance on all outcomes assessed. Similar trends are likely to be found in dentistry. Estimates on the delay in adoption of new knowledge are also of concern. Balas and Boren (2000) reported in their review that the delay in adoption of new biomedical knowledge averaged 17 years. Ironically, some new technologies and procedures of questionable benefit to the patient are very rapidly adopted. The aggressive marketing of products and concepts not adequately tested magnifies the problem of keeping up-to-date. The explosion of new knowledge and short half-life of existing biomedical knowledge also has serious implications for dental education. To keep up-to-date, we estimate that approximately one-half of the clinical curricula of a dental school would need to be changed every seven to ten years. There is evidence that dental schools are not keeping up. For example, Klasser and Greene (2007) reported that 23% of the 53 United States and Canadian dental schools surveyed continued to endorse occlusal adjustment for prevention and treatment of TMD despite opposing conclusions presented in three review articles and despite the conclusions of a 2003 Cochrane Systematic Review (Koh & Robinson, 2003) which stated, “Occlusal adjustment cannot be recommended for the management or prevention of TMD.” Given the rapid development of new knowledge and short half-life in all areas of dentistry, it is likely that much of the curricula of many schools may not be current. In summary, there is evidence that the explosion of new knowledge and the turnover of knowledge have exceeded the capacity of current science transfer mechanisms. Both practitioners and dental schools are facing serious challenges in their efforts to keep up-to-date. New models of education and science transfer must be explored to address the situation, which is projected to worsen with the continued exponential growth of new knowledge and products.

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)0.042
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.059
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0420.040
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0030.003
Science and technology studies0.0050.005
Scholarly communication0.0110.008
Open science0.0050.014
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0590.016

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 head0.268
GPT teacher head0.491
Teacher spread0.223 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2010
Admission routes1
Has abstractyes

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