MétaCan
Menu
Back to cohort

Do Defendants Pay What Juries Award? Post‐Verdict Haircuts in Texas Medical Malpractice Cases, 1988–2003

2007· article· en· W1974865494 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Empirical Legal Studies · 2007
Typearticle
Languageen
FieldHealth Professions
TopicMedical Malpractice and Liability Issues
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsVerdictJuryPlaintiffMedical malpracticeDamagesPsychologyLawMedicineActuarial scienceMalpracticeBusinessPolitical science

Abstract

fetched live from OpenAlex

Legal scholars, legislators, policy advocates, and the news media frequently use jury verdicts to draw conclusions about the performance of the tort system. However, actual payouts can differ greatly from verdicts. We report evidence on post‐verdict payouts from the most comprehensive longitudinal study of matched jury verdicts and payouts. Using data on all insured medical malpractice claims in Texas from 1988–2003 in which the plaintiff received at least $25,000 (in 1988 dollars) following a jury trial, we find that most jury awards received “haircuts.” Seventy‐five percent of plaintiffs received a payout less than the adjusted verdict (jury verdict plus prejudgment and postjudgment interest), 20 percent received the adjusted verdict (within ± 2 percent), and 5 percent received more than the adjusted verdict. Overall, plaintiffs received a mean (median) per‐case haircut of 29 percent (19 percent), and an aggregate haircut of 56 percent, relative to the adjusted verdict. The larger the verdict, the more likely and larger the haircut. For cases with a positive adjusted verdict under $100,000, 47 percent of plaintiffs received a haircut, with a mean (median) per‐case haircut of 8 percent (2 percent). For cases with an adjusted verdict larger than $2.5 million, 98 percent of plaintiffs received a haircut with a mean (median) per‐case haircut of 56 percent (61 percent). Insurance policy limits are the most important factor in explaining haircuts. Caps on damages in death cases and caps on punitive damages are also important, but defendants often paid substantially less than the adjusted allowed verdict. Remittitur accounts for a small percentage of the haircuts. Punitive damage awards have only a small effect on payouts. Out‐of‐pocket payments by physicians are rare, never large, and usually unrelated to punitive damage awards. Most cases settle, presumably in the shadow of the outcome if the case were to be tried. That outcome is not the jury award, but the actual post‐verdict payout. Because defendants rarely pay what juries award, jury verdicts alone do not provide a sufficient basis for claims about the performance of the tort system.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.110
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.110
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.135
GPT teacher head0.522
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it