Best-Practice Recommendations for Chiropractic Care for Pregnant and Postpartum Patients: Results of a Consensus Process
Bibliographic record
Abstract
OBJECTIVE: The purpose of this project was to develop a best-practices document on chiropractic care for pregnant and postpartum patients with low back pain (LBP), pelvic girdle pain (PGP), or a combination. METHODS: A modified Delphi consensus process was conducted. A multidisciplinary steering committee of 11 health care professionals developed 71 seed statements based on their clinical experience and relevant literature. A total of 78 panelists from 7 countries were asked to rate the recommendations (70 chiropractors and representatives from 4 other health professions). Consensus was reached when at least 80% of the panelists deemed the statement to be appropriate along with a median response of at least 7 on a 9-point scale. RESULTS: Consensus was reached on 71 statements after 3 rounds of distribution. Statements included informed consent and risks, multidisciplinary care, key components regarding LBP during pregnancy, PGP during pregnancy and combined pain during pregnancy, as well as key components regarding postpartum LBP, PGP, and combined pain. Examination, diagnostic imaging, interventions, and lifestyle factors statements are included. CONCLUSION: An expert panel convened to develop the first best-practice consensus document on chiropractic care for pregnant and postpartum patients with LBP or PGP. The document consists of 71 statements on chiropractic care for pregnant and postpartum patients with LBP and PGP.
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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 | 0.381 | 0.330 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.004 |
| 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".