{"doi":"10.1093/pm/pnae069","title":"Achieving two-part harmony: standardizing pain-related phenotypes and outcomes","abstract":"Military service members and veterans are especially vulnerable to developing chronic pain conditions, given their high risk of musculoskeletal injuries and exposure to combat-related trauma. Approximately half of active-duty military service members experience significant pain, especially women.1 Indeed, persistent back pain is among the most common drivers of health care use and interruption of combat duty.1 These realities highlight the need to develop and deploy effective strategies for prevention and management of pain, particularly nonpharmacological treatments, which are effective first-line therapies for military and veteran personnel but are underutilized in clinical practice. Pragmatic trials can help to address this implementation gap. In this Commentary, we describe the benefits and the process of harmonizing data collection efforts through use of standardized measures across a pragmatic trial network (the Pain Management Collaboratory, PMC). Though harmonization poses significant challenges (eg, possible delays in trial initiation as measures are harmonized, slight elevation of trial costs, potential increases in participant burden), its benefits are likely to outweigh the costs. As a large trial network, the PMC provides a model of how combining pragmatic trial design with collaborative tools and multidisciplinary expertise can advance the science and impact of nonpharmacological pain management research in real-world settings. We believe that harmonizing phenotypic and outcome data collection across similar trial networks has the potential to magnify the results emerging from those studies, support comparisons across populations, and promote the wide adoption of evidence-based care. Data are maximally useful if collected in a standardized manner to facilitate reproducibility, subgroup analyses, data pooling, and cross-study comparisons. Moreover, variability in outcome measures across clinical trials hinders the field’s ability to conduct evaluations of treatment efficacy and effectiveness.2 Harmonized data collection is especially important for research studies related to conditions such as chronic pain, in which many different types of participant data are collected, including many measurements that fall within the category of patient-reported outcomes.3 Collectively, the use of a standard set of validated outcome measures across network trials: (1) facilitates the process of developing individual study research protocols; (2) ensures that all trials within a network will provide potentially actionable clinical information; (3) simplifies the process of designing and reviewing research proposals, manuscripts, and published articles within the network; (4) enhances study reproducibility; (5) provides a basis for determining treatment outcomes that constitute clinically important differences; (6) permits pooling of data from different studies to enhance power and generalizability; and (7) aids future systematic reviews and meta-analyses. In addition, standardizing a core set of outcome domains encourages investigation and reporting of outcomes that are relevant to research partners and end users, so that dissemination of trial-related data does not involve selective presentation of some outcomes while excluding others. Doing so is especially critical in trials for chronic pain, which affects numerous life domains including those rated as very important by individuals experiencing chronic pain (eg, physical and emotional functioning).4 Notably, the PMC identified strategies for optimizing pragmatic clinical trials for pain management for military service members and veterans; development and use of a harmonized set of outcome measures endorsed by pain experts is an important element of this ongoing process. Together with harmonizing outcome data collection, standardizing the assessment of patient phenotypes provides robust advantages. It is widely recognized that there is significant variability in treatment outc","journal":"Pain Medicine","year":2024,"id":479150,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9497,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":536559,"name":"Mary Geda","orcid":"0000-0002-9129-4432","position":1,"is_corresponding":false},{"id":536560,"name":"Diana J. Burgess","orcid":"0000-0003-1139-7739","position":2,"is_corresponding":false},{"id":793957,"name":"Alison F. Davis","orcid":"0000-0002-8610-6580","position":3,"is_corresponding":false},{"id":377020,"name":"Lynn DeBar","orcid":"0000-0001-9191-9706","position":4,"is_corresponding":false},{"id":1271905,"name":"Natassja Pal","orcid":null,"position":5,"is_corresponding":false},{"id":98374,"name":"Peter Peduzzi","orcid":"0000-0002-7634-6804","position":6,"is_corresponding":false},{"id":360884,"name":"Diana J. Burgess","orcid":"0000-0002-3266-1132","position":7,"is_corresponding":false},{"id":25029,"name":"Robert B. Wallace","orcid":"0000-0001-6040-3803","position":8,"is_corresponding":false},{"id":793959,"name":"Stephen L. Luther","orcid":"0000-0001-7524-7380","position":9,"is_corresponding":false},{"id":1148377,"name":"Robert J. Edwards","orcid":"0000-0002-4392-8998","position":0,"is_corresponding":true}],"reference_count":11,"raw_metadata":null,"created_at":"2026-07-19T02:06:50.355747Z","pmid":"39514875","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}