Number to Treat Analysis for Planned Neck Dissection after Organ Preservation Therapy with Advanced Neck Disease
Bibliographic record
Abstract
OBJECTIVE: To perform a number to treat analysis for planned neck dissection after organ preservation protocols (OPPs) in N2-3 neck disease for head and neck cancer. METHODS: We performed a literature review from 1993 to the present and collected aggregate data to produce the following four variables: (1) percentage of N2-3 necks still harbouring cancer after radiotherapy in OPPs (C); (2) percentage of regional recurrence after planned neck dissection (P); (3) unsuccessful salvage rate in patients in whom a watch and wait strategy for neck disease was employed (S); and (4) the mortality rate of planned neck dissection (M). The number to treat can be estimated as 1/((C x S + C x M) - (P + M)) RESULTS: The number to treat in this analysis was 4.4. SUMMARY: In organ preservation therapy with N2-3 disease, one needs to perform 4.4 neck dissections to prevent one fatal regional recurrence. Although this calculation does have some inherent biases and errors, it may form the basis for an informed discussion with patients faced with the option of planned neck dissection.
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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 | 0.030 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".