Comorbidity, co‐occurrence, continuum: what’s in a name?
Bibliographic record
Abstract
BACKGROUND: Comorbidity, co-occurrence and continuum are three terms used when referring to developmental problems such as Developmental Coordination Disorder (DCD), but they can be confusing and misleading. Further, the terms can be upsetting to parents, and are not always helpful in guiding the selection of clinical interventions. GOALS: The main purpose of this paper is to question some of the terminology we employ when referring to DCD and other developmental problems. A secondary purpose is to discuss some of the conceptual frameworks that have been proposed that attempt to address the issue of the interrelationships among developmental problems. APPROACH: The terminology is examined by first referring to the basic dictionary definitions. Second, data we have published that relate to the issues of co-occurrence and continuum are reviewed in light of the terminology questions. Finally, we review some alternative conceptual frameworks which more accurately describe the relationships among developmental problems. CONCLUSION: The term 'comorbidity' has limited relevance to developmental problems, and its use is questionable. In contrast, co-occurrence and continuum are more useful terms to use in regard to developmental problems. Concepts such as atypical brain development and minor neurological dysfunction provide some possible explanations for the increased levels of co-occurrence of developmental disorders in children who are more severely affected.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".