Standardized and individualized care: do they complement or oppose each other?
Bibliographic record
Abstract
EDITOR–The overall aim of the Common Data Elements (CDE) project1 was to standardize data collection and assessment in studies of children and young people with cerebral palsy (CP). Dr Fairhurst's commentary raised the following question: can standardized care coexist with personalized care without losing the flexible art of medicine?2 Standardization promotes the application of evidence-based medicine (or the best available evidence) in a systematic way, to ensure patients receive effective care. We need less variance in clinical practice to achieve more consistent outcomes and improve quality of care and patient safety. On the other hand, personalization of care relies on health professionals knowing their patients well and treating them as unique individuals. Personalization improves the patient experience and increases their engagement and health knowledge. While standardization of clinical care – using universal frameworks, CDEs, toolbox of measures, core sets, care pathways – is valuable to support evidence-based practices, the experience from the child's and his or her family's points of view can still be personalized. It is important to note that with standardized care there are still decisions to be made based on the child's and family's preferences and choices. Setting goals and priorities for intervention, pain management techniques, and type and time of introduction of assistive technologies are examples where clients' decision-making takes place to meet their personalized needs. Hence, we can standardize and personalize care at the same time. The recommended CDEs for children and young adults with CP standardize data collection for this population. In CP, standardization and personalization can complement each other. Most importantly, standardization can enhance personalization by eliminating the unnecessary time and cost of using low quality assessment tools, or ignoring opportunities to obtain relevant clinical information, which in turn minimizes complications or inappropriate treatment that does not adhere to best evidence-informed practices. Health professionals often make assessment and treatment decisions based on their unique clinical education and experiences. Standardization compensates for this variation in history and experience and harmonizes assessment, evaluation, and reporting of outcomes, ultimately facilitating informative comparisons across research studies and therapeutic interventions. Finally, we propose that the essence of good empathic care is based on the personal connection and mutual respect developed between health professionals and the child and family. While standardization of care has the potential to relieve actual workload and to allow for more positive and productive in-person connections. Future CP CDE revisions will incorporate additional information that includes client perspectives, preferences, and choices in a systematic way, to enhance the standardized tools proposed by the current recommendations in CP CDEs version 1.0.
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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 | 0.036 | 0.230 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.011 |
| Scholarly communication | 0.007 | 0.013 |
| Open science | 0.008 | 0.003 |
| Research integrity | 0.027 | 0.046 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".