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External validation of a shortened screening tool using individual participant data meta-analysis: A case study of the Patient Health Questionnaire-Dep-4
Daphna Harel, Brooke Levis, Ying Sun, Felix Fischer, John P. A. Ioannidis, Pim Cuijpers, Scott B. Patten, Roy C. Ziegelstein, Sarah Markham, Andrea Benedetti, Brett D. Thombs, Chen He, Yin Wu, Ankur Krishnan, Parash Mani Bhandari, Dipika Neupane, Zelalem Negeri, Mahrukh Imran, Danielle B. Rice, Kira E. Riehm, Marleine Azar, A.H. Levis, Jill Boruff, Simon Gilbody, Lorie A. Kloda, Dagmar Amtmann, Liat Ayalon, Hamid Reza Baradaran, Anna Beraldi, Çharles N. Bernstein, Arvin Bhana, Ryna Imma Buji, Marcos Hortes Nisihara Chagas, Juliana C.N. Chan, Lai Fong Chan, Dixon Chibanda, Aaron Conway, Federico M. Daray, Janneke M. de Man‐van Ginkel, Crisanto Díez, Sally Field, Jane Fisher, Daniel Fung, Emily Garman, Alan J. Flisher, Bizu Gelaye, Leila Gholizadeh, Lorna J. Gibson, Eric Green, Brian J. Hall, Liisa Hantsoo, Emily E. Haroz, Martin Härter, Ulrich Hegerl, Leanne Hides, Stevan E. Hobfoll, Simone Honikman, Marie Hudson, Thomas Hyphantis, Masatoshi Inagaki, Hong Jin Jeon, Nathalie Jetté, Mohammad E. Khamseh, Sebastian Köhler, Brandon A. Kohrt, Yunxin Kwan, Femke Lamers, Ma. Asunción Lara, Holly Frances Levin-Aspenson, Shen‐Ing Liu, Manote Lotrakul, Sônia Regina Loureiro, Bernd Löwe, Nagendra P. Luitel, Crick Lund, Ruth Ann Marrie, Brian P. Marx, Sherina Mohd Sidik, Tiago N. Munhoz, Kumiko Muramatsu, Juliet Nakku, Laura Navarrete, Flávia de Lima Osório, Philippe Persoons, Angelo Picardi, Stephanie L. Pugh, Terence J. Quinn, Elmārs Rancāns, Sujit D. Rathod, Katrin Reuter, Heather Rowe, Iná S. Santos, Miranda T. Schram, Juwita Shaaban, Eileen H. Shinn, Lena Spangenberg, Lesley Stafford, Sharon C. Sung, Keiko Suzuki, Pei Lin Lynnette Tan, Martin Taylor‐Rowan, Thach Tran, Christina M. van der Feltz‐Cornelis, Thandi van Heyningen, Henk van Weert, Lynne I. Wagner, JianLi Wang, David Watson, Karen Wynter, Mitsuhiko Yamada, Qing Zhi Zeng, Yuying Zhang
Bibliographic record
VenueMethods · 2021
Typereview
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsMcGill UniversityMcGill University Health CentreUniversity of CalgaryJewish General Hospital
FundersNational Institute of Mental HealthEuropean Regional Development FundCanadian Institutes of Health ResearchTakeda CanadaPfizer CanadaScleroderma Society of OntarioJanssen CanadaSandoz CanadaShanghai Municipal Health and Family Planning CommissionH. Lundbeck A/SChinese Diabetes SocietyAchmeaUniversity of Cape TownNational Health Research InstitutesNational Health and Medical Research CouncilJewish General HospitalUniversidade de MacauUniversiti Sains MalaysiaMedical Research CouncilServierUniversiti Putra MalaysiaNational Institutes of HealthTehran University of Medical Sciences and Health ServicesEisaiMinistry of Science, ICT and Future PlanningConselho Nacional de Desenvolvimento Científico e TecnológicoUniversity of AucklandMinistero della SaluteConsejo Nacional de Ciencia y TecnologíaDepartment for International DevelopmentMinistry of Education, Science and TechnologyBundesministerium für Bildung und ForschungDepartment of Education and Early Childhood Development, State Government of VictoriaUniversity of Technology SydneyUniversiteit van AmsterdamUniversität HeidelbergUniversidade de São PauloMultiple Sclerosis SocietyLance Armstrong FoundationNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversity of MelbourneCilagBanco SantanderResearch ManitobaJanssen PharmaceuticalsAmgen CanadaNovo NordiskDeutsche RentenversicherungNational Institute on Minority Health and Health DisparitiesEuropean CommissionAbbVieNational Research Foundation of KoreaCumming School of Medicine, University of CalgaryPennsylvania Department of HealthEli Lilly and CompanyCanadian Arthritis NetworkU.S. Department of DefenseFundação de Amparo à Pesquisa do Estado do Rio Grande do SulSanofiZonMwAbbVie CanadaAlberta Innovates - Health SolutionsDuke Global Health Institute, Duke UniversityMahidol UniversityNational Alliance for Research on Schizophrenia and DepressionCrohn's and Colitis CanadaMinistry of Health, Labour and WelfareAlberta Health ServicesNational Institute on Disability and Rehabilitation ResearchPfizerIcahn School of Medicine at Mount SinaiDepartment of Social Services, Australian GovernmentBristol-Myers SquibbGovernment of the United KingdomOhio Board of RegentsAustralian GovernmentNational Cancer InstituteSigma Theta Tau InternationalAlberta InnovatesNational Research FoundationAmgenHealth Foundation LimburgGrand Challenges CanadaUnited States Agency for International DevelopmentMylanQueensland University of TechnologyArmstrong Foundation
KeywordsCutoffPatient Health QuestionnaireEquivalence (formal languages)RespondentMedicineMeta-analysisReceiver operating characteristicDepressive symptomsInternal medicineMathematicsPsychiatryPhysics
Abstract
No abstract in any covered source. Its absence is recorded, not treated as a negative.
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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.
metaresearch head score (Codex)0.162
metaresearch head score (Gemma)0.269
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.838
Threshold uncertainty score0.859
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.162 | 0.269 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.022 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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.
Opus teacher head0.898
GPT teacher head0.686
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation status
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.
Study designMeta-analysis
DomainMethods
GenreEmpirical
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".
Quick stats
Citations6
Published2021
Admission routes2
Has abstractno