Bibliographic record
Abstract
“How should I apply this information?” Physical therapists (PTs) contribute to decisions about flatfoot management as part of the child's health care team. Children with more complex impairments related to flatfoot are often referred to PTs, heightening the need for best practice information and evidence to guide decision making about when to use foot orthoses (FOs). An evidence-informed understanding can assist clinicians in identifying those who may benefit from FOs, to avoid inappropriate intervention. Flatfoot classification is important to judge whether the foot posture is following a trajectory of an asymptomatic developmental (physiological) or of a symptomatic (painful) nondevelopmental (pathological) flatfoot. By surveying 34 PTs in Canada and discussing their responses in the context of the research literature, the authors found that objective physical examination and differentiation between developmental and pathological flatfoot can help clinicians identify suitable candidates for FOs, monitor foot posture over time, and evaluate treatment effectiveness. An evidence-informed approach to assessment and intervention is important to improve management in children with pediatric flatfoot. “What should I be mindful about when applying this information?” Physical therapists have a clear role on the health care team with respect to flatfoot assessment, determining appropriate physical therapy intervention and monitoring for changes in foot posture, function, and symptoms. Despite the lack of high-quality evidence available to guide these decisions, the variation in constructing the FOs by an orthotist or a PT, or ordering them commercially, illustrates a lack of clear consensus regarding the clinical application of FOs, also reflecting a lack of best practice information in the literature. Use of currently available best evidence-based tools to guide decisions about whether the foot is flexible or rigid, and whether it is symptomatic or asymptomatic, is recommended. Åsa Bartonek, PT, PhD Karolinska Institutet Stockholm, Sweden
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".