Bibliographic record
Abstract
Letters5 September 2006Reanalysis of Survival of Oscar WinnersDonald A. Redelmeier, MD and Sheldon M. Singh, BScDonald A. Redelmeier, MDFrom Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5, Canada.Search for more papers by this author and Sheldon M. Singh, BScFrom Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5, Canada.Search for more papers by this authorAuthor, Article, and Disclosure Informationhttps://doi.org/10.7326/0003-4819-145-5-200609050-00015 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail TO THE EDITOR: In this issue, Sylvestre and colleagues (1) correctly comment that survival statistics are fallible. The primary analysis in our study (2) was based on the Kaplan–Meier method because life expectancy is the preferred metric in medical decision analysis (3). Our article also provided 40 other secondary analyses to explore different models because no one statistic is ideal. Sylvestre and colleagues argue that the multivariate-adjusted Cox proportional hazards model with a time-varying step function is preferred over our primary analysis approach, do not discuss the limitations of such models, and intimate that other models give an unfair advantage. ...References1. Sylvestre M, Huszti E, Hanley JA. Do Oscar winners live longer than less successful peers? A reanalysis of the evidence. Ann Intern Med. 2006;145:361-3. LinkGoogle Scholar2. Redelmeier DA, Singh SM. Survival in Academy Award–winning actors and actresses. Ann Intern Med. 2001;134:955-62. [PMID: 11352696] LinkGoogle Scholar3. Sox HC, Blatt MA, Higgins MC, Marton KI. Medical Decision Making. Toronto: Butterworths; 1988:182-4. Google Scholar4. Redelmeier DA, Singh SM. Longevity of screenwriters who win an academy award: longitudinal study. BMJ. 2001;323:1491-6. [PMID: 11751368] CrossrefMedlineGoogle Scholar5. Redelmeier DA, Singh SM. Association between mortality and occupation among movie directors and actors. Am J Med. 2003;115:400-3. [PMID: 14553877] CrossrefMedlineGoogle Scholar6. Fisher LD, Lin DY. Time-dependent covariates in the Cox proportional-hazards regression model. Annu Rev Public Health. 1999;20:145-57. [PMID: 10352854] CrossrefMedlineGoogle Scholar7. Therneau TM, Grambsch PM. Modeling Survival Data: Extending the Cox Model. New York: Springer; 2000:231-2. Google Scholar8. Kalbfleisch JD, Prentice RL. The Statistical Analysis of Failure Time Data. 2nd ed. Hoboken, NJ: J Wiley; 2002:196-208. Google Scholar9. Allison PD. Survival Analysis Using SAS: A Practical Guide. Cary, NC: The SAS Institute Inc; 2004:111-84. Google Scholar Author, Article, and Disclosure InformationAffiliations: From Sunnybrook Health Sciences Centre, Toronto, Ontario M4N 3M5, Canada.University of Toronto, Toronto, Ontario M4N 3M5, CanadaDisclosures: None disclosed. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoSurvival in Academy Award–Winning Actors and Actresses Donald A. Redelmeier and Sheldon M. Singh Reanalysis of Survival of Oscar Winners Steven Goodman and Harold C. Sox Do Oscar Winners Live Longer than Less Successful Peers? A Reanalysis of the Evidence Marie-Pierre Sylvestre , Ella Huszti , and James A. Hanley Metrics Cited ByOpportunities and Challenges in Using Epidemiologic Methods to Monitor Drug Safety in the Era of Large Automated Health DatabasesTime-dependent study entries and exposures in cohort studies can easily be sources of different and avoidable types of biasStatistics in the NewsTwo Pitfalls in Survival Analyses of Time-Dependent Exposure: A Case Study in a Cohort of Oscar NomineesAn easy mathematical proof showed that time-dependent bias inevitably leads to biased effect estimationReply to the comment by Drs. Girard et al. 5 September 2006Volume 145, Issue 5Page: 392KeywordsConflicts of interestDatabasesDecision analysisLife expectancyScientistsSurvival analysis ePublished: 5 September 2006 Issue Published: 5 September 2006 CopyrightCopyright © 2006 by American College of Physicians. All Rights Reserved.PDF DownloadLoading ...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".