Bibliographic record
Abstract
Shakespeare knew that a name is more than the title we assign to a person or thing — it can include the qualities of a subject so that it can easily be identified and put in context. The expanded promotion of health information means that non-dental professionals and the public are exposed to dental terminology, so it behooves dentists to take care with names. One that has evolved over time is the label applied to tooth decay in very young children. It has been known as bottle mouth, baby bottle tooth decay, nursing caries, labial caries and, most recently, early childhood caries (ECC). At a workshop on ECC in 1999,1 participants defined the disease and set the parameters for its most rampant version: severe early childhood caries. Can you see the trend? Many of these labels presumed an etiology. We now find that the most recent term, ECC, leaves something to be desired. At a conference on ECC in Calgary in September (see page 897), the collective wisdom of delegates representing diverse areas of interest, from medicine, nursing, social work, immigrant and refugee aid, poverty reduction and teaching, told us that ECC is a misnomer that perpetuates the unfortunate concept that the mouth is separate from the body. Worse yet, the term may delay recognition and access to treatment. Over the last 3 decades we fought hard for recognition of the importance of a healthy oral environment for everyone, not only cardiac, cancer, diabetic, transplant and special needs patients. Those of us currently engaged in the battle know how much work it takes to change a paradigm. The idea that good oral health is linked to good general health is gaining recognition in many areas of society. Just as people no longer shrug about smoking or drinking and driving, we no longer ignore the health risks related to dental diseases. The battle’s tide is turning in our favour, but we still have work to do. Young patients, the next generation of Canadians, must be our priority. Children are the most vulnerable members of society, yet they are being ravaged by some of the worst levels of dental disease many pediatric dentists have ever seen. This disease, which is mostly preventable, affects all strata of the socioeconomic scale, although it is concentrated in immigrant and refugee groups, First Nations residents and those trapped in rising poverty levels. If society is judged by how it treats our most vulnerable, we have some Dr. Shwart Email: luke.shwart@ calgaryhealthregion.ca
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".