Corrigendum: Predicting Outcomes From Radical Radiotherapy for Non-small Cell Lung Cancer: A Systematic Review of the Existing Literature
Bibliographic record
Abstract
Dr Gerard WallsCentre for Cancer Research & Cell BiologyQueen’s University BelfastLisburn RoadBelfastBT9 7AB31st October 2018To whom it may concern, RE: ‘Predicting outcomes from radical radiotherapy for non-small cell lung cancer: an evidence map of the literature’. It has been brought to my attention as the corresponding author of the above manuscript that the declaration of conflicts has been inaccurately stated for one author. I was prompted during the submission process to make reference to the involvement of several authors with a company. I adapted the example statement provided: …Fang Qi, Sai Zhao, Jun Xia, Mohammed T. Ansari are employed by Systematic Review Solutions Ltd. I can confirm that none of the other authors have any conflicts of interest to declare. In truth, this statement should read: …Fang Qi, Sai Zhao and Jun Xia are employed by Systematic Review Solutions Ltd. Mohammed T. Ansari is contracted as a Consultant by Systematic Review Solutions Ltd. I can confirm that none of the other authors have conflicts of interest to declare. May I ask that the conflict of interest statement is updated please? Many thanks for your time and help and sincere apologies for the inconvenience caused.Yours sincerely, Gerard WallsDr Gerard Walls MB BCH BAO MRCP Dip(Ed)Academic Clinical Fellow in Clinical OncologyBCH Cancer Centre & Queen's University Belfast
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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 | 0.018 | 0.264 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.083 | 0.017 |
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".