Pain and suffering disability index
Bibliographic record
Abstract
PURPOSE OF REVIEW: This report summarizes the increasing financial resources required to deal with personal injury tort cases and medical malpractice. The largest single component in personal injury torts is noneconomic damages, which encompasses 'pain and suffering' and punitive damage, the latter of which comprises only a small percentage. Overall, noneconomic damages account for 24% of the greater than US$250 billion spent annually on personal injury torts. RECENT FINDINGS: A pain and suffering disability index has been developed that quantifies the loss of life's value attributable to personal injury. Based upon time-tradeoff utility analysis, the value loss is predicated upon the values of people who have experienced the same degree of disability or injury as the plaintiff, only outside the courtroom environs. It is believed that the pain and suffering disability index will readily identify frivolous, personal injury torts, decrease the number of frivolous, personal injury torts, markedly decrease the variance of noneconomic tort settlements, facilitate the earlier settlement of personal injury tort cases, and decrease the proportion of personal injury tort cases progressing to jury trial. SUMMARY: The pain and suffering disability index is a novel instrument that quantifies the 'pain and suffering' associated with a personal injury tort according to the values of patients who have experienced a similar injury outside the courtroom environs.
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 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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".