Beyond the Musculoskeletal System: Considering Whole-Systems Readiness for Running Postpartum
Bibliographic record
Abstract
Background: Postpartum women frequently engage in running. In the absence of official guidance on returning-to-running postpartum, physical therapists rely on clinical experience alongside the available literature. Subsequently, the traditional evaluation of postpartum readiness for running tends to focus on musculoskeletal factors. This clinical commentary addresses how to evaluate and manage postpartum return-to-running in a systematic order by discussing relevant whole-systems considerations beyond the musculoskeletal system, while also highlighting possible interactions between relevant considerations. Discussion: Using a whole-systems biopsychosocial approach, physical therapists should consider the following when managing and evaluating readiness to return-to-running: physical deconditioning, changes to body mass, sleeping patterns, breastfeeding, relative energy deficiency in sport, postpartum fatigue and thyroid autoimmunity, fear of movement, psychological well-being, and socioeconomic considerations. Undertaking a risk-benefit analysis on a case-by-case basis using clinical reasoning to determine readiness to return-to-running postpartum should incorporate these considerations and their possible interactions, alongside considerations of a musculoskeletal evaluation and graded exercise progression. Conclusions: Return-to-running postpartum requires an individualized, whole-systems biopsychosocial approach with graded exercise progression, similar to the management of return to sport following musculoskeletal injuries. A video abstract for this article is available at: http://links.lww.com/JWHPT/A51
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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 | 0.005 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".