Work productivity loss in mild to moderate COPD: lessons learned from the CanCOLD study
Bibliographic record
Abstract
Little attention has been given to the impact of chronic obstructive pulmonary disease (COPD) on work productivity loss. Individuals with COPD are at risk of reduced working hours, absenteeism, presenteeism and early retirement [1]. Studies have been focused mostly on patients attending outpatient clinics [2], which exclude individuals with undiagnosed COPD, thus limiting the external validity of the findings. There are very few population-based cohort studies [3–6], few reports on presenteeism [5], and a lack of objective measures to define COPD [6]. There would be value in knowing the extent of work productivity loss in individuals with mild COPD, or those who are yet undiagnosed. This could further translate into the allocation of health management programmes in the workplace. COPD patients with high symptom burden or CAT ≥10 have an increased likelihood of experiencing work productivity loss The authors thank the men and women who participated in the study and individuals in the CanCOLD Collaborative Research Group. Author contributions: R. de Sousa Sena contributed to the conception and implementation of the study, analysis of the data, and writing of the manuscript. J. Bourbeau contributed to the study conception and design, implementation and acquisition of the data, and writing and revision of the article. S. Ahmed contributed to the study conception and design and the writing and revision of the article. W.C. Tan, L. Labonté, S.D. Aaron, A. Benedetti, K.R. Chapman, B. Walker, J.M. Fitzgerald, P. Hernandez, F. Maltais, D.D. Marciniuk, D.E. O'Donnell and D.D. Sin contributed to the acquisition of data and revision of the article. P.Z. Li contributed to the analysis and interpretation of the data. All authors approved the final version of the manuscript. Members of the CanCOLD Collaborative Research Group are as follows. Executive Committee: Jean Bourbeau (McGill University, Montreal, Canada); Wan C. Tan, J. Mark FitzGerald; Don Sin (UBC, Vancouver, Canada); Darcy Marciniuk (University of Saskatoon, Saskatoon, Canada); Dennis E. O'Donnell (Queen's University, Kingston, Canada); Paul Hernandez (Dalhousie University, Halifax, Canada); Kenneth R. Chapman (University of Toronto, Toronto, Canada); Robert Cowie (University of Calgary, Calgary, Canada); Shawn Aaron (University of Ottawa, Ottawa, Canada); F. Maltais (University of Laval, Quebec City, Canada). International Advisory Board: Jonathon Samet (Keck School of Medicine of USC, Los Angeles, CA); Milo Puhan (John Hopkins School of Public Health, Baltimore, MD); Qutayba Hamid (McGill University, Montreal, Canada); James C. Hogg (UBC James Hogg Research Center, Vancouver, Canada). Operations Center: Jean Bourbeau (PI), Carole Jabet, Palmina Mancino, (McGill University, Montreal, Canada); Wan C. Tan (co-PI), Don Sin, Sheena Tam, Jeremy Road, Joe Comeau, Adrian Png, Harvey Coxson, Jonathon Leipsic, Cameron Hague (University of British Columbia James Hogg Research Center, Vancouver, Canada). Economic Core: Mohsen Sadatsafavi (University of British Columbia, Vancouver, Canada). Public Health Core: Teresa To, Andrea Gershon (University of Toronto, Toronto, Canada). Data Management and Quality Control: Wan C. Tan, Harvey Coxson (UBC, Vancouver, Canada); Jean Bourbeau, Pei Zhi Li, Zhi Song, Yvan Fortier, Andrea Benedetti, Dennis Jensen (McGill University, Montreal, Canada). Field Centers: Wan C. Tan (Vancouver PI), Christine Lo, Sarah Cheng, Cindy Fung, Nancy Haynes, Junior Chuang, Licong Li, Selva Bayat, Amanda Wong, Zoe Alavi, Catherine Peng, Bin Zhao, Nathalie Scott-Hsiung, Tasha Nadirshaw (UBC James Hogg Research Center, Vancouver, Canada); Jean Bourbeau (Montreal PI), Palmina Mancino, David Latreille, Jacinthe Baril, Laura Labonté (McGill University, Montreal, Canada); Kenneth Chapman (Toronto PI), Patricia McClean, Nadeen Audisho (University of Toronto, Toronto, Canada); R. Cowie and B. Walter (Calgary PI), Ann Cowie, Curtis Dumonceaux, Lisette Machado (University of Calgary, Calgary, Canada); Paul Hernandez (Halifax PI), Scott Fulton, Kristen Osterling (Dalhousie University, Halifax, Canada); Shawn Aaron (Ottawa PI), Kathy Vandemheen, Gay Pratt, Amanda Bergeron (University of Ottawa, Ottawa, Canada); Denis O'Donnell (Kingston PI), Matthew McNeil, Kate Whelan (Queen's University, Kingston, Canada); François Maltais (Quebec PI), Cynthia Brouillard (Université Laval, Quebec City, Canada); Darcy Marciniuk (Saskatoon PI), Ron Clemens, Janet Baran (University of Saskatoon, Saskatoon, Canada).
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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 | 0.035 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".